A Comprehensive Study of Recommender Systems: Prospects and Challenges

International Journal of Scientific & Engineering Research, Volume 6, Issue 8, August-2015 699 ISSN 2229-5518 A COMPREHENSIVE STUDY OF RECOMMENDER SYSTEMS: PROSPECTS AND CHALLENGES

Adetoba B. Tiwalola., Yekini N. Asafe

1Department of Computer Science, Yaba College of Technology, Lagos Nigeria 2Department of Computer Engineering, Yaba College of Technology, Lagos Nigeria

Abstract - Recommender systems (RSs) automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. Recommender Systems are software tools and techniques aimed at providing suggestion to support users in various decision-making processes. Development of recommender systems is a multi-disciplinary effort which involves experts from various fields such as Artificial intelligence (AI), Human Computer Interaction (HCI), Information Technology (IT), Data Mining, Statistics, Adaptive User Interfaces, Decision Support Systems (DSS), Marketing, or Consumer Behaviour. Recommender systems have proven to be valuable means for online users to cope with information overload and various techniques for recommendation algorithms have been proposed and successfully deployed in commercial environments. In this paper, a comprehensive study of recommendation systems and various approaches are provided with their major strengths and limitations thereby providing future research possibilities in recommendation systems.

Keywords: Artificial intelligence (AI), Recommender systems (RSs), Computer Interaction (HCI), decision- making processes, Data Mining,

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personalisation for each customer (Prasad & I. INTRODUCTION Kumari, 2012). Recommender Systems or Recommendation Non-personalized recommender systems Systems (RSs) are software tools and techniques recommend products to customers based on what aimed at providingIJSER suggestion to support users in other customers have said about the products on various decision-making processes such as what average. The recommendations are independent of items to buy, what music to listen, or what news to the customer, so each customer gets the same read (Ricci, 2012). recommendations. Non-personalized recommender In the simplest form, personalized systems are automatic, because they require little recommendations are offered as ranked lists of customer effort to generate the recommendations items. In performing the ranking, RSs predict what and are momentary. These recommendations are the most suitable products or services are, based on completely independent of the particular customer the interest and constraints of the users. In order to targeted by the recommender system. For example, complete these computational task, RSs collect Amazon.com and Moviefinder.com websites are from users their preferences (interest), which are treated as non-personalized recommender systems either explicitly stated, e.g. ratings for products, or (Prasad & Kumari, 2012). are inferred by interpreting user actions. For RSs development initiated from a rather simple instance, a RS may consider the navigation to a observation: individuals often rely on particular product page as an implicit sign of recommendations provided by others in making preference for the items shown on that page. routine or daily decisions (Mahmood & Ricci, Personalized recommender systems are used by E- 2009). The recommendations were for items that commerce sites to suggest products to their similar users (those with similar tastes) had liked. customers. The products can be recommended This approach is termed collaborative-filtering and based on the top sellers of a site, demographics of its rationale is that if the active user agreed in the the customer, or analysis of the past buying past with some users, then the other behaviour of the customer as a prediction for future recommendations coming from these similar users buying behaviour, for example eBay (Ricci et. al., should be relevant as well and of interest to the 2011). These techniques help the sites spread over active user (Ricci et al., 2010). the World Wide Web to adapt itself to each Recommender systems have proven to be valuable customer requirements thus enabling individual means for online users to cope with information

IJSER © 2015 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 6, Issue 8, August-2015 700 ISSN 2229-5518 overload; and various techniques for . There are opportunities for researcher to obtain recommendation algorithms have been proposed useful information from dedicated conferences and successfully deployed in commercial and workshop related to the field e.g ACM environments. Existing recommender systems use SIGIR Special Interest Group on Information collaborative filtering or content-based or hybrid Retrieval (SIGIR), User Modelling, Adaptation methods that combine both techniques. Several and Personalization (UMAP); and ACM’s data mining techniques (such as Similarity Special Interest Group on Management of Data measures, Sampling, Dimensionality Reduction, (SIGMOD). Classification, Association-Rule-Mining (ARM) . It’s an opportunity to develop syllabus for and Clustering) are frequently used for graduates and undergraduate students in to recommendation technology to enhance on-line support e-commerce in developing countries. business. The above reasons motivate the author of this work. A. Problem Statement The explosive growth and variety of information available on the Web and the rapid II. CLASSIFICATION OF RECOMMENDER introduction of new e-business services (buying SYSTEMS products, product comparison, auction, etc.) There are various types of recommendation frequently overwhelmed or confused users, leading techniques, some are knowledge poor, i.e., they use them to make poor decisions. RSs have proved, in very simple and basic data, such as user recent years, to be a valuable means for coping ratings/evaluations for items. Others are much with this information overload problem. Although more knowledge dependent, e.g., using ontological many different approaches to recommender descriptions of the users or the items or constraints systems have been developed over past years but or social relations and activities of the users. the interest in this area still remains high due to Recommendation systems use a number of growing demand on practical applications (real life different technologies. We can classify these applications), which are able to provide approaches into two: Traditional Recommendation personalized recommendations and to deal with Approach and Modern Recommendation Approach information overload. These growing demands (Wanaskar et. al., 2013). More emphasise will be pose some key challenges to recommender systems on the traditional approaches – Collaborative and to deal with these problems many advanced filtering, Content-based, Knowledge-based, techniques are proposed, like content-based Hybrid, Community-based and Demographic- collaborative filtering, clustering-based filtering, filtering approach. combining item-based and user-based similarity and many more. Despite all these advances, recommender systemsIJSER still require improvement and thus becoming a rich research area.

B. Objectives The objective of this paper is to present an overview of recommendation systems, the various techniques used and also propose future research topics. The content of this paper mainly includes the following: . To explain recommendation system as a tool in solving information overload problem. . To provide a comprehensive study on various techniques/approaches used in recommender systems especially the traditional methods with their major strengths and limitations. . To provide future direction in recommendation systems.

C. Motivation The interest in RSs has dramatically increased based on the following reasons: . E-commerce is at its infancy in developing countries and RSs play an important role in this area.

A key advantage of this approach is that it does not A. Traditional Recommendation Approach rely on machine analyzable content and therefore it Collaborative filtering: Collaborative filtering is capable of accurately recommending complex became one of the most researched techniques of items such as movies without requiring any recommender systems since this approach was "understanding" of the item itself. Many algorithms mentioned and described by Paul Resnick and Hal have been used in measuring user similarity or item Varian in 1997 (Resnik & Varian, 1997). It is also similarity in recommender systems. For instance, the most widely used and successful methods for the k-nearest neighbour (k-NN) approach (Sarwar implementing RSs (Oliver & Pujol, 2011). The idea et al., 2000) and the Pearson Correlation. When building a model from a user's profile, distinction is of collaborative filtering is, finding the users in a often made between implicit and explicit forms community that share appreciations (Takacs et. al., of data collection. 2009). If two users have same or almost same Examples of implicit data collection are listed rated items in common, then they have similar below: tastes. Such users build a group or a so called . Observing items a user view in an online store. neighbourhood. A user gets recommendations to . Analyzing item/user viewing times choose items that he/she has not rated before, but . Keeping records of items that a user purchases that were already positively rated by users in online. his/her neighborhood. Collaborative filtering is Examples of explicit data collection are listed widely used in e-commerce. Customers can rate below: books, songs, movies and then get . Asking users to rate an item on a sliding scale. recommendations regarding those issues in future. . Asking users to rank a collection of items from Moreover collaborative filtering is utilized in favourite to least favourite. browsing of certain documents e.g. documents . Presenting two items to a user and asking among scientific works, articles, and magazines him/her to choose the one that is better. . (Orlando et al., 2004). Asking user to create a list of items that he/she Figures 1 and 2 show the user-based and item- likes. based methods respectively: One of the most famous examples of collaborative filtering is item-to-item collaborative filtering (people who buy x also buy y), an algorithm popularized by Amazon.com's recommender system. Other examples include: . Last.fm recommends music based on a comparison of the listening habits of similar users. IJSER. Facebook, MySpace, LinkedIn, and other social networks use collaborative filtering to recommend new friends, groups, and other social connections (by examining the network of connections between a user and their friends) (Ricci et al., 2011). Collaborative filtering recommender approach can be further divided into two categories (Bobadilla et al., 2012): Memory-based Collaborative Filtering (Neighbourhood based); and Model-based Collaborative Filtering. In memory-based Figure 1: User-based approach collaborative filtering systems, a set of items or users is generated according to the relevance of user or item. This system works on the ratings whether implicit or explicit. The user-item ratings are stored in the system and helps in generating the list of items or users to be recommended. There are two types of memory-based collaborative filtering approach known as item-based collaborative filtering and user-based collaborative filtering (Adomavicius et. al., 2005; Su & Khoshgoftaar, 2009). The user-based collaborative filtering approaches evaluate the interest of a user for an item using the Figure 2: Item-based approach (Wanaskar et ratings given by other users for that item. On the al., 2013)

IJSER © 2015 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 6, Issue 8, August-2015 702 ISSN 2229-5518 other hand, item-based collaborative filtering correlations are both stored on the recommender predicts the rating of an item for a user, based on platform to ensure real time results. the similar items, liked by the user. The algorithm can be in this form: User-based recommendations: If User A likes Items 1,2,3,4, and 5, And User B likes Items 1,2,3, and 4 Then User B is quite likely to also like Item 5 ( see fig 1) Item-based recommendations: If Users who purchase item 1 are also disproportionately likely to purchase item 2 And User A purchased item 1 Then User A will probably be interested in item 2 ( see fig 2) Model-based collaborative filtering systems uses the user item rating stored in the system to learn a predictive model. The basic idea behind this approach is to model the user item interactions with main characteristic and features extraction. The Fig 3: Collaborative Recommender System model is then trained by using these data. This Architecture (Sarwar et al., 2000) model then comes in handy to predict ratings of user for new items. Several different machine Advantages of Collaborative filtering learning algorithms are used in this approach like Recommender systems: The main advantages of Bayesian Clustering (Breeze, 1998), Probabilistic collaborative filtering recommender systems are Latent Semantic Analysis (PLSA) Hofmann, 2003), that they are more effective when it comes to Latent Dirichlet Allocation (LDA) Blei et al., customer satisfaction as they recommend the most 2003), Maximum Entropy (Zitnick et al., 2004), appropriate items to users (Koenigstein et al., Boltzmann Machines (Salakhutginov et al., 2007), 2011). The collaborative filtering algorithms are Support Vector Machines (SVM) (Grcar et al., designed such that the accuracy of their prediction 2005), Singular Value Decomposition (SVD) increases tremendously over items as more user (Paterek, 2007). preferences are added to the database, irrespective Collaborative filtering Recommender systems of the size of the database (Lopez-Nores et al., application: One of most popular applications in 2012). the field of collaborative filtering is Amazon’s recommendation IJSERsystem. Amazon uses Disadvantages of Collaborative filtering recommendations for marketing campaigns and Recommender systems: Collaborative filtering personal adaptation of its homepage. Customers approaches often suffer from the following have the possibility to receive individual problems: cold start, scalability, sparsity and Gray suggestion on Amazon products, based on the sheep (Lee et al., 2007) articles they purchased previously. For the reason . Cold Start: These systems often require a large that ordinary CF algorithms cannot scale Amazon’s amount of existing data on a user in order to massive datasets, they developed their own Item- make accurate recommendations. To-Item Collaborative Filtering method (Sarwar et . Scalability: In many of the environments that al., 2000). The main difference to traditional item- these systems make recommendations in, there based CF techniques is that the Amazon algorithm are millions of users and products. Thus, a large prunes items which have no common customers or amount of computation power is often belong to unlike product catalogs. However, in necessary to calculate recommendations. order to provide real time recommendations, the . Sparsity: The number of items sold on major e- expensive item-similarity matrix is computed commerce sites is extremely large. The most offline. active users will only have rated a small subset Figure 3 presents the interaction of a user with an of the overall database. Thus, even the most online collaborative recommender system through popular items have very few ratings. a web interface. In order to suggest products to a . Gray sheep problem: The main drawback of this user, web server and recommender need to method is that the system would not be very communicate with each other. Usually, the web effective when user preferences change server application forwards user feedback to the unexpectedly, as the system still focuses on past recommender, and receives personalized interests of the user. It is also known as gray recommendations in return. User ratings and item sheep problem (Lopez-nores et al., 2012. A particular type of collaborative filtering

algorithm uses matrix factorization, a low-rank the component is to represent the content of matrix approximation technique (Sachan & items (e.g. documents, Web pages, news, Richariya, 2013). product descriptions, etc.) coming from information sources in a form suitable for the Content-based filtering: Another common approach next processing steps. Data items are analyzed when designing recommender systems is content- by feature extraction techniques in order to shift based filtering (Gunawardana & Shani, 2009). item representation from the original These are based on information about and information space to the target one (e.g.Web characteristics of the items that are going to be pages represented as keyword vectors). This recommended. In other words, these algorithms try representation is the input to the profile learner to recommend items that are similar to those that a and filtering component; user liked in the past (or is examining in the . Profile Learner: This module collects data present). In particular, various candidate representative of the user preferences and tries preferences are compared with items previously to generalize this data, in order to construct the rated by the user and the best-matching items are user profile. Usually, the generalization strategy recommended. This approach has its roots in is realized through machine learning techniques, information retrieval and information filtering which are able to infer a model of user interests research (Wikipedia, 2013). starting from items liked or disliked in the past. Basically, these methods use an item profile (i.e., a For instance, the PROFILE LEARNER of a set of discrete attributes and features) Web page recommender can implement a characterizing the item within the system. The relevance feedback method (Rocchio, 1971) in system creates a content-based profile of users which the learning technique combines vectors based on a weighted vector of item features. The of positive and negative examples into a weights denote the importance of each feature to prototype vector representing the user profile. the user and can be computed from individually Training examples are Web pages on which a rated content vectors using a variety of techniques. positive or negative feedback has been provided Simple approaches use the average values of the by the user; rated item vector while other sophisticated methods . Filtering Component: This module exploits the use machine learning techniques such as Bayesian user profile to suggest relevant items by Classifiers, cluster analysis, decision trees, matching the profile representation against that and artificial neural networks in order to estimate of items to be recommended. The result is a the probability that the user is going to like the binary or continuous relevance judgment item. Direct feedback from a user, usually in the (computed using some similarity metrics form of a like or dislike button, can be used to (Herlocker et al., 2004), the latter case resulting assign higher or lower weights on the importance in a ranked list of potentially interesting items. of certain attributesIJSER (using Rocchio Classification In the above mentioned example, the matching or other similar techniques) (Krishna & Devi, is realized by computing the cosine similarity 2012). between the prototype vector and the item A key issue with content-based filtering is whether vectors. the system is able to learn user preferences from The first step of the recommendation process is the user's actions regarding one content source and use one performed by the content analyzer, which them across other content types. When the system usually borrows techniques from Information is limited to recommending content of the same Retrieval systems (Baeza-Yates & Ribeiro-Neto, type as the user is already using, the value from the 1999). Item descriptions coming from Information recommendation system is significantly less than Source are processed by the content analyzer, that when other content types from other services can extracts features from unstructured text to produce be recommended. For example, recommending a structured item representation, stored in the news articles based on browsing of news is useful, repository Represented Items. but it's much more useful when music, videos, In order to construct and update the profile of the products, discussions etc. from different services active user ua (user for which recommendations can be recommended based on news browsing. must be provided) her reactions to items are A high level architecture of a content based collected in some way and recorded in the recommender system is depicted in Figure 4. The repository Feedback. These reactions, called recommendation process is performed in three annotations or feedback, together with the related steps, each of which is handled by a separate item descriptions, are exploited during the process component: of learning a model useful to predict the actual . Content analyser: When information has no relevance of newly presented items. Users can also structure (e.g. text), some kind of pre- explicitly define their areas of interest as an initial processing step is needed to extract structured profile without providing any feedback. relevant information. The main responsibility of

Fig 4: High level architecture of a Content-based Recommender (Ricci et al., 2011) sparsity problem. A good example of hybrid Content-based Applications: Pandora Radio is a systems is Netflix which makes recommendations popular example of a content-based recommender by comparing the watching and searching habits of system that plays music with similar characteristics similar users (i.e. collaborative filtering) as well as to that of a song provided by the user as an initial by offering movies that share characteristics with seed. There are also a large number of content- films that a user has rated highly (content-based based recommender systems aimed at providing filtering). A hybrid recommender system is one movie recommendations, a few such examples that combines multiple techniques together to include Rotten Tomatoes, Internet Movie achieve some synergy between them. Hybrid Database, Jinni, Rovi Corporation. recommender system is used to describe any recommender system that combines multiple Advantages of Content-based recommender recommendation techniques together to produce its systems: The main advantage of this method is that output. There is no reason why several different it does not depend on the user ratings of items in techniques of the same type could not be the database and hence, even if the database does hybridized, for example, two different content- not contain user preferences, the prediction based recommenders could work together, and a accuracy is not affected. Even if the user number of projects have investigated this type of preferences change,IJSER it has the capacity to adjust its hybrid: NewsDude, which uses both naive Bayes recommendations in a short span of time. and kNN classifiers in its news recommendations is just one example (Burke, 2007). Disadvantages of Content based recommender systems: The main drawback of this approach is the Hybrid recommender systems combine both of the need to know all the details of an item really well, earlier methods to produce better results by even where the features of the item is stored in the involving all the advantages of the two techniques database in a way where it cannot be retrieved and by removing their drawbacks at the same time directly. (Bobadilla et al., 2012). Burke (2002) introduced taxonomy for the hybrid recommendation systems. B. Hybrid Recommender Systems He classified them into seven categories, weighted, Hybrid approach combines both collaborative switching, mixed, feature combination, feature filtering and content-based filtering, this can be augmentation, cascade, and meta-level. implemented in many ways: by making . Weighted hybrid – This hybrid combines scores collaborative-based and content-based predictions from each component using linear formula. separately and then combining them; by adding Therefore, components must be able to produce collaborative-based capabilities to a content-based its recommendation score which can be linearly approach, and vice versa; or by unifying the combinable. Also, the components have to be approaches into one model. Several studies consistent relative accuracy across the product empirically compare the performance of the hybrid space and to perform uniformly. with the pure collaborative and content-based . Switching hybrid – The issue of this hybrid is methods and demonstrate that the hybrid methods selecting one recommender among candidates. can provide more accurate recommendations than This selection is made according to the situation pure approaches. These methods can also be used it is experiencing. The criterion for the to overcome some of the common problems in selection like confidence value or external recommender systems such as cold start and the criteria should exist and the components might

have different performance with different This architecture is based on both collaborative situations. filtering and content-based filtering methods by . Mixed hybrid – This is a hybrid which is based using multi-based clustering, illustrated in Fig. 5. It on the merging and presentation of multiple includes two major components: an off-line ranked lists into one. Each component of this component and an online component. The off-line hybrid should be able to produce component is a batch processing unit that runs recommendation lists with ranks and the core periodically. It consists of two modules as follows. algorithm of mixed hybrid merges them into a . The training module is the main process that single ranked list. The issue here is how the new generates the predicted data rating by using rank scores should be produced. multi-based clustering method. A dual based . Feature combination hybrid – There exist two group, a similar users based group and a very different recommendation components for similar items based group is formed as credible this hybrid, contributing and actual information sources. Then, it analyzes each recommender. The actual recommender works group’s influence on the target users from the with data modified by the contributing one. The target items. This approach takes advantage of contributing one injects features of one source user correlations and item correlations to the source of the other component. embedded in the user-item matrix. Hence the . Feature augmentation hybrid – This is similar to new items can be included in the the feature combination hybrids but different in recommendations. The predicted data rating that the contributor generates new features. It is are then generated and stored in database. The more flexible and adds smaller dimension than predicted data rating is computed by feature combination method. combining the item-based predicted ratings . Cascade hybrid – This is a tie breaker. The and the user-based predicted ratings to make a secondary recommender is just a tie breaker and high accuracy predicted ratings on those items does refinements. for users. . Meta-level hybrid – For this, contributing and • In the clustering module, we cluster the actual recommenders exist but the former one predicted rating matrix to find groups of like- completely replaces the data for the latter one, minded users. These groups are small in size not just part of it. compared to the original set, thus making the technique scalable. C. An Hybrid Recommender System Framework IJSER

Figure 5: Hybrid Recommender System Architecture (Puntheeranurak & Tsuji, 2009)

on a demographic profile of the user. D. Demographic-based recommender systems Recommended products can be produced for This system provides recommendations based different demographic niches, by combining the

IJSER © 2015 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 6, Issue 8, August-2015 706 ISSN 2229-5518 ratings of users in those niches. Many websites the long-term description of user but it does adopt simple and effective personalization recommend on the evaluation of users’ need and solutions based on demographics (Wang & optional set and calculate the preference of the Reinders, 2003; Montainer et. al., 2003; Gemmis product to user. This approach doesn’t suffers from et. al, 2009). For example, users are dispatched to cold-start problem or overspecialization because it particular Websites based on their language or is independent of other user, rating and statistical country. Or suggestions may be customized evaluation (Burke, 2000). This is the main according to the age of the user. Other advantage is advantage of using this approach. However, this that the quality of recommendation improves over approach heavily depends upon user profile. This the span of time as it builds the profile for user approach needs three types of knowledge: preferences (Gemmis et al., 2009). knowledge about the users, knowledge about the The main disadvantage of this approach is that it items and knowledge about the matching between suffers from cold-start problem because it needs the item and user’s need and this is one its user preferences to recommend better and also it disadvantage (Shishehchi et. al., 2011). Most needs huge demographic information. It also knowledge based recommender systems are case suffers from gray-sheep problem (Montainer et al., based (Mahmood & Ricci, 2007). 2003; Gemmis et. al, 2009). F. Community-based recommender system E. Knowledge-based system This is a recommender system where system Knowledge-based recommender system recommends items, based on preferences of user’s integrates the knowledge of users and products to friends. It has been observed that people tend to do recommendation (Burke, 2000). It is a rely more on recommendations from their friends recommender system that suggests products based rather than anonymous individuals (Shimon et al., on inferences about a user’s needs and preferences. 2007). This observation generates a rising interest This knowledge will sometimes contain explicit in community-based systems; they are usually functional knowledge about how certain product referred to as social recommender systems (Sinha features meet user needs (Burke, 2013). This & Swearingen, 2001). system does not recommend based on generalizing Table 1: Comparison of Recommender System Techniques Mentioned With Advantages and Disadvantages Techniques Advantages/Strengths Disadvantages/Weaknesses Author & Year of Publishing Collaborative . No domain knowledge required. . New-user problem (Cold-start . Burke, 1999 Filtering . Quality of recommendation problem). . Sharma & Gera, 2013. increases over time. . New-item problem (First rater . Wanaskar et. al., 2013. IJSER problem). . Khoshgoftaar, 2009. . Sparsity problem. . Su and Khoshgoftaar, . Gray sheep problem. 2009 . Scalability problem. Content based . -No domain knowledge required. . New-user problem (Cold-start Hu & Pu, 2011 Filtering . -Quality of recommendation problem). increases over time. . Limited content analysis . -New Item recommendation problem. . Overspecialization Hybrid based . No domain knowledge required. Ali & Ghani, 2012 Filtering . Quality of recommendation increases over time. . New Item recommendation . No Sparsity problem Demographic . No domain knowledge required. . New-user problem (Cold-start . Montaner et. al., 2003. . Quality of recommendation problem). Gray sheep. . Gemmis et. al., 2009 increases over time. . Need demographic . No new-item problem. information Knowledge . No cold-start problem. . Needs domain knowledge. Burke, 1999. based Filtering . No overspecialization problem. . Does not learn over time Burke, 2000. . No sparsity problem. . Prone to preference changes. Community . No domain knowledge required. . New-user problem (Cold-start based . Quality of recommendation problem). Herlocker et. al., 2004 increases over time. . Gray sheep. Lopez-Nores et. al., 2009. . No new-item problem . Need community . Information

G. Modern Recommendation Approaches neighbourhood is an important part of Context-based recommender system: The recommending process of collaborative majority of existing approaches to recommender recommender systems. Similarities of two users are systems focus on recommending the most relevant discovered based on their appreciation of items. items to individual users and do not take into But similar appreciations in one domain do not consideration any contextual information, such as surely mean that in another domain valuations are time, place and the company of other people (e.g., similar as well (Winoto & Tang, 2008). for watching movies or dining out). It is also important to incorporate the contextual information Peer-to-Peer Approaches: The recommender into the recommendation process in order to systems with P2P approaches are decentralized. recommend items to users under certain Each peer can relate itself to a group of other peers circumstances. For example, a travel recommender with same interests and get recommendations from system would provide a vacation recommendation the users of that group. Recommendations can also in the winter that can be very different from the one be given based on the history of a peer. in the summer. Therefore, accurate prediction of Decentralization of recommender system can solve consumer preferences depends upon the degree to the scalability problem (Shavitt et al., 2010). which the recommender system has incorporated the relevant contextual information into a Cross-lingual Approaches: The recommender recommendation method (Ricci et. al., 2010). system based on cross-lingual approach lets the users receive recommendations to the items that Semantic Based Approaches: Most of the have descriptions in languages they don’t speak descriptions of items, users in recommender and understand. Yang, Chen and Wu purposed an systems and the rest of the web are presented in the approach for a cross lingual news group web in a textual form. Using tags and keywords recommendation. The main idea is to map both text without any semantic meanings doesn’t improve and keywords in different languages into a single the accuracy of recommendations in all cases, as feature space, that is to say a probability some keywords may be homonyms. That is why distribution over latent topics. From the understanding and structuring of text is a very descriptions of items the system parses keywords significant part recommendation. Traditional text than translates them in one defined language using mining approaches that base on lexical and dictionaries. After that, using collaborative or other syntactical analysis show descriptions that can be filtering, the system gives recommendations to understood by a user but not a computer or a users (Yang et al., 2008) recommender system. That was a reason of creating new text mining techniques that were III. EXISTING WORK based on semantic analysis. Recommender systems There have been a lot of researches in the area with such techniquesIJSER are called semantic based of recommender systems. Several recommender recommender systems. The performance of systems have been developed so far according to semantic recommender systems are based on different recommendation techniques discussed knowledge based usually defined as a concept above. These recommender systems are related to diagram (like taxonomy) or ontology (Wang and various fields of applications, such as news, music, Kong, 2007). e-commerce, movies, etc. Each domain presents different problems that require different solutions. Cross-Domain Based Approaches: Finding Table 2 illustrates 10 different domains where similar users and building an accurate Recommendation applications exist.

TABLE 2: Recommender Systems with Different Domains (Sharma & Suman, 2011) Domain Risk Churn Heterogeneous Preferences Interaction Scrutiny Examples Recommendation Style Technology E-commerce Low High High Stable Implicit Not Amazon.com, eBay Collaborative required Filtering Financial High Low Low Stable Explicit Required Koba4MS (Felfernig, 2005) ) Knowledge Based services and FSAdviser (Felfernig & Kiener, 2005) Life insurance Job Search High Low Low Stable Explicit Required CASPER(Lee, 2009) and Content based Recruiting (Keim et. al., 2006) Movie Low Low Low Stable Implicit Not Netflix (Paterek, 2007) Collaborative and required INTIMATE (Bollacker et. al., 98) Content based Movies2Go (Mak et. al.,2003) Music Low Low Low Stable Implicit Not Pandora and (Hayes, 2000) Content based required Hybrid News Low High Low Stable Implicit Not Yahoo News (Billus &Pazzani, 2006) Content based required ACR Collaborative – news (Mobasher et al, 2000) and Filtering Google news (DAS et. al, 2007) INFOrmer NewsDude Real Estate High Low Stable Explicit Required RentMe (Burke, 2000) Knowledge based FlatFinder (Viappiani, 2007

Scientific Low Low Low Stable Explicit Not QuickStep system (Middleton, 2002) Content based Research Implicit required Citeseer (Boone, 1998) papers IJSER Software Low Low Low Stable Explicit Required Castro-Herrera et. al., 2008 Hybrid and Content Engineering Implicit based Tourism High Low Low Unstable Explicit Required Travel Recommender Content based (Ricci, 2002) Knowledge based TV Program Low Low Low Unstable Implicit Not AVTAR (Blanco-Fernandez et. al., Content based Explicit required 2008) Web Page Low High High Unstable Implicit Not Zaiane et. al., 2004 Collaborative Recommender required Letna (Letizia, 1995) Filtering Hybrid

Recommendation domains can be distinguished by to determine whether the users have similar the degree of risk that a user incurs in accepting a taste in music and it computes the weighted recommendation. Risk determines the user’s average of the ratings for that particular artist, tolerance for false positives among the song or album using the information in them recommendations. In some domains, false (Shardananda & Maes, 1995). negatives may also be important if there is a cost or . Content: personalized newspapers, risk associated with not considering some options. recommendation for documents, High-risk domains are generally considered under recommendations of Web pages, e-learning knowledge-based recommendation. applications, and e-mail filters. A high churn domain is one in which items come . E-commerce: recommendations for consumers and go rapidly. In such a domain, a recommender of products to buy, such as books, cameras, system faces a continual stream of new items to be PCs etc. Learning Intelligent Book integrated into its knowledge sources. This greatly Recommending Agent (LIBRA) used by increases the sparsity of any kind of opinion data, Amazon, makes use of the database which as new items will necessarily have been seen by holds the information of all the books very few users. extracted from the web pages at Amazon (Burke, 2002). The actual texts of the items are User preferences can also have varying degrees of not used in this system. Amazon.com uses duration. For example, when one’s favourite topic diversification algorithms to improve basketball team is in a big tournament, stories recommendations (Ziegler et al., 2005). about it become highly preferred, but if they are . Services: recommendations of travel services, knocked out or when the tournament is over, the recommendation of experts for consultation, user’s preferences will change. recommendation of houses to rent, or Scrutiny is also a good predictor of knowledge- matchmaking services. based recommendation. Heterogeneous domains are handled largely with collaborative V. Recommender Systems Algorithms recommendation. Webpage recommendation looks In this paper we will consider the three popular a bit contradictory when we consider high churn algorithms used in recommendation approaches and preference instability, which would seem to and these are: Pearson Correlation, Cosine militate against collaborative methods. However, Similarity and Item-to-Item Similarity. database size can compensate for preference Pearson Correlation is used to compare the linear instability and these recommenders collect large dependence between two variables (Almazro et al., amounts of implicit preference data in each session. 2010). In context recommender systems, it is used It has been observed that the recommender systems to compare the ratings of the items which are rated with social knowledge, requires high heterogeneity. by a reader or a user (u) and the number of IJSERneighbours (n). In the area of e-commerce famous literature includes both content-based and collaborative approaches. In content-based approach, the idea is to detect items that are most “similar” to the user’s existing profile. A user profile is composed of (1) his/her previous transaction history, such as what he/she viewed or purchased before. After the user Where, I is the set of items rated by both users; rn,i profile is set up, how to determine similarity is the rating given to item i by user n. between an item and the profile is the key The predicted rating for u over item j, where j has challenge. Various approaches have been been rated by both u and n can be calculated as developed (Zhang & Zhang, 2010), such as cosine follows: similarity, Bayesian classifiers, clustering, etc.

IV. APPLICATIONS OF RECOMMENDER (2) SYSTEMS Where Pu,j is the prediction for active user u for Recommender systems applications are mostly item j, w(u,i) is the similarity between users u and i, common in the following areas and K is the neighbourhood or set of similar users. . Entertainment: recommendations for movies, music, and IPTV. Cosine Similarity: The cosine similarity or vector Ringo is an online music recommendation similarity (Gunawardana, 2009) is used to measure system which uses social information filtering similarity using the cosine angle formed by the to make personalized recommendation. Using frequency vectors (for example NewsWeeder). In correlation algorithm (Pearson Correlation memory based collaborative filtering algorithms, technique), Ringo compares the user profiles the similarity between two documents are

IJSER © 2015 http://www.ijser.org International Journal of Scientific & Engineering Research, Volume 6, Issue 8, August-2015 710 ISSN 2229-5518 measured using this technique. The document is considered as a vector of frequencies of words and Item to Item Similarity: This measure is used to the cosine angle between the two vectors of compute the similarity between items (Linden et frequencies are calculated to determine similarity. al., 2003; Jojic et al., 2011). Most of the times, the In the calculation of cosine similarity, only positive maximum likelihood estimate (MLE) is used to ratings are considered and negative ratings are calculate conditional probability of items. The rejected. The formula is shown below: conditional probability of items in binary usage can be calculated as below:

(3) (5)

Where Iu.i is the set of items which both the users Where, Ix represents the number of users who had rated positively. Ii is the item which is rated used item x, and the number of users who had rated positively by the user i. Only positive ratings are both i1 and i2 is represented by I i1,i2 considered for computing the cosine similarity. Using this, the predicted score for a user is computed as below:

(4) Table 3: Summary of recommender systems and their algorithms (Ali & Ghani, 2012) IJSER depend on the user to create their profiles GroupLens (Adomavicious & Tuzhilin, 2005) is altogether (Lang, 1995). an architecture used for distributing ratings, which can be modified by anybody who wants to alter or VI. EVALUATION METRICS OF improve a news client to use predicted scores or to RECOMMENDER SYSTEMS make a news client to allow entry of evaluations. It Several metrics are used to evaluate was introduced to mine the reactions of people who recommendation algorithms (Herlocker et al., read news articles and the architecture was 2004). The quality of a recommender system can successful in scaling up to a very large number of be evaluated by comparing recommendations to a users and ratings. The system employed test set of known user ratings. These systems are collaborative filtering methods on vast number of typically measured using predictive accuracy news articles (or content) present in Netnews in metrics, where the predicted ratings are directly order to help users to find useful articles in a much compared to actual user ratings. The most easier way. commonly used metric in the literature is Mean Jester 2.0 (Gupta et al., 2011) is a web based Absolute Error (MAE) which is defined as the recommender system for jokes, which is used to average absolute difference between predicted predict the jokes which might interest the user. ratings and actual ratings, given by: NewsWeeder: This is a text recommender which uses the words in the texts as features. It is basically a Netnews filtering system which lets the user rate his interest level (from 1 to 5) for each (6) article and learns their user profile based on the information obtained. This eliminates the need to

Where Pu,i is the predicted rating for user u on item to handle this continuously growing demand. Some i, ru,i is the actual rating, and N is the total number of the recommender system algorithms deal with of ratings in the test set. the computations which increase with growing number of users and items. In CF computations A related commonly-used metric, Root Mean grow exponentially and get expensive, sometimes Squared Error (RMSE), puts more emphasis on leading to inaccurate results. Methods proposed for larger absolute errors, and is given by: handling this scalability problem and speeding up recommendation formulation are based on approximation mechanisms. Even if they improve (7) performance, most of the time they result in Predictive accuracy metrics treat all items equally. accuracy reduction (Papagelis et al., 2005). However, for most recommender systems we are primarily concerned with accurately predicting the Over Specialization Problem: Users are restricted items a user will like. As such, researchers often to getting recommendations which resemble those view recommending as predicting good, i.e. items already known or defined in their profiles in some with high ratings versus bad or poorly-rated items. cases, and it is termed as over specialization In the context of Information Retrieval (IR), problem (Chen et al., 2011). It prevents user from identifying the good from the background of bad discovering new items and other available options. items can be viewed as discriminating between However, diversity of recommendations is a “relevant” and “irrelevant” items; and as such, desirable feature of all recommendation systems. standard IR measures, like Precision, Recall and After solving the problem using genetic algorithms, Area Under the ROC (Receiver operating user will be provided with a set of different and a characteristics) Curve (AUC) (Bamber, 1975) or wide range of alternatives. precision recall can be utilized. These, and several other measures, such as F1-measure, Pearson’s Sparsity: Sparsity problem is one of the major product-moment correlation, Kendall’s τ, mean problems encountered by recommender systems; average precision, half-life utility, and normalized and data sparsity has great influence on the quality distance-based performance measure are discussed of recommendation. Generally, data of system like in more detail by Herlocker et al. (2004). MovieLens is represented in form of user-item matrix populated by ratings given to movies and as VII. CHALLENGES OF EXISTING number of users and items increase the matrix RSs dimensions and sparsity evolves. The main reason Various techniques used in a recommender behind data sparsity is that most users do not rate system experiences some of the hurdles that may most of the items and the available ratings are be described in terms of basic problems such as: usually sparse. Collaborative filtering suffers from IJSERthis problem because it is dependent on the rating Cold-Start: Cold start problem refers to the matrix in most cases. Many researchers have situation when a new user or item just enters the attempted to reduce this problem; still this area system. There are three kinds of cold start demands more research (Sharma & Gera, 2013). problems: new user problem, new item problem and new system problem (Sharma & Mann, 2013). Trust: The voices of people with a short history In such cases, it is really very difficult to provide may not be that relevant as the voices of those who recommendation as in case of new user, there is have rich history in their profiles. The issue of trust very less information about user that is available arises towards evaluations of a certain customer. and also for a new item, no ratings are usually The problem could be solved by distribution of available and thus collaborative filtering cannot priorities to the users (Herlocker et al., 2000; make useful recommendations as in the case where Bonhard et al., 2006; Cramer et al., 2008; Pu and we have new item as well as new user. However, Chen, 2006). content-based methods can provide recommendations if there is a new item as they do Privacy: Privacy has been the most important not depend on any previous rating information of problem. In order to receive the most accurate and other users. These problems can be solved using correct recommendation, the system must acquire the hybrid approach. the most amount of information possible about the user, including demographic data, and data about Scalability: Scalability is the property of a system the location of a particular user. Naturally, the which indicates its ability to handle growing question of reliability, security and confidentiality amount of information in a graceful manner. With of the given information arises. Many online shops enormous growth in information over internet, it is offer effective protection of privacy of the users by obvious that the recommender systems are having utilizing specialized algorithms and programs an explosion of data and thus it is a great challenge (Wanaskar et al., 2013).

measures that more effectively determine Fraud: As Recommender Systems are being performance of recommender systems. increasingly adopted by commercial websites, they Also, new application areas for recommender have started to play a significant role in affecting systems emerge with the popularity of the Social the profitability of sellers. This has led to many Web. This Social Web therefore provides huge unscrupulous vendors engaging in different forms opportunities for recommender technology and in of fraud to game recommender systems for their turn recommender technologies can play a part in benefit. Typically, they attempt to inflate the fuelling the success of the Social Web perceived desirability of their own products (push phenomenon. attacks) or lower the ratings of their competitors Finally, privacy-protection considerations are also (nuke attacks). These types of attack have been a challenge. Recommender algorithms can identify broadly studied as shilling attacks (Shyong et al., patterns individuals might not even know exist. A 2004), or profile injection attacks (Burke et al., recent example is the case of a large company that 2005). Such attacks usually involve setting up could calculate a pregnancy-prediction score based dummy profiles, and assume different amounts of on purchasing habits. Through the use of targeted knowledge about the system. For instance, the ads, a father was surprised to learn that his teenage average attack (Shyong et. al., 2004) assumes daughter was pregnant. The company's predictor knowledge of the average rating for each item; and was so accurate that it could predict a prospective the attacker assigns values randomly distributed mother's due date based on products she purchased. around this average, along with a high rating for the item being pushed. 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