JOURNAL OF CRITICAL REVIEWS

ISSN- 2394-5125 VOL 7, ISSUE 10, 2020 Social Network Recommender System Based on Collaborative Filtering Techniques

Mr. Pitamber Adhikari1, Mrs. Renuka Sharma2

1Dept. of Computer Science and Engineering 2Dept. of Information Technology Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh [email protected], [email protected]

Received: 6 February 2020 Revised and Accepted: 21 May 2020

ABSTRACT: Recommender Systems (RS) are used in different areas for applications like recommending products to customers. During this paper, this paper proposes a new approach for Recommender systems that employ the users’ social network to produce better recommendations for media items like YouTube, Amazon, Netflix, and Pandora. This paper has study personalized item recommendations within an enterprise social media application suite that features blogs, bookmarks, communities, Wiki, and shared files. Recommendations are supported by two of the core elements of social media people and tags. Relationship information among people, tags, and items, is collected and aggregated across different sources within the enterprise. This paper evaluated our recommended system through an intensive user study. Results show an excellent interest ratio for the tag- based recommended than for the people-based Recommender and even better performance for a combined Recommender. Tags applied to the user by the people are found to be highly effective in representing that user’s topics of interest. KEYWORDS: Recommender System, Social Network, Collaborative Filtering, Machine Learning.

I. INTRODUCTION Social media has been enjoying an excellent deal of success in recent years, with a lot of users visiting social sites like LinkedIn, Instagram, Facebook, WhatsApp, for social networking, WordPress for blogging, Twitter for micro-blogging, Netflix, Flickr, Pandora radio, and YouTube for image and video sharing,[1] respectively, large amount of social news reading, and distinctive for social bookmarking. This social media networking relies principally on their users to make, and contribute content to annotate others' content with tagging, item ratings, and comments to create online relationships and to join online communities. To overcome information overload, Recommender systems became a key tool for providing users with personalized recommendations on items like movies, music, books, news, and sites. Intrigued by many practical applications, researchers have developed algorithms and systems over the last decade. A number of them are commercialized by online web services like Alibaba.com, Flipcart.com, Amazon.com, Netflix.com, and IMDb.com[2]. The recommender systems predict user preferences (often represented as numeric ratings) for new items that supported the user's past ratings on other items. There are typically three types of algorithms for recommender systems -- content-based methods and collaborative filtering and hybrid recommender. Content-based recommender systems measure the similarity of the recommended item to those that a target user likes or dislikes supported item attributes. On the opposite hand, the collaborative filtering method finds users with tastes that are almost like the target users supported their past ratings. Collaborative filtering will then make recommendations to the target user-supported the opinions of these similar users. Finding the most relevant information and items has been a challenge for an extended time[3]. Based on this user requirement, recommendation systems have become one of the foremost popular techniques to solve this problem. Almost, all recommendation systems try to make predictions about the preference of every user-supported the preferences of a group of comparable users through content-based filtering, collaborative filtering or combination of both. Figure 1 shows recommender system's types.

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ISSN- 2394-5125 VOL 7, ISSUE 10, 2020

Figure 1: Types of Recommender System

The collaborative filtering algorithm is the prevalent recommendation system which has been wont to identify users that may be characterized as the same consistent with the logged history of past user behaviors. Generally, a collaborative filtering algorithm uses a set of user profiles to find the most relevant information,[4] items, products, services or etc. for these users. The specific user achieves a recommendation that supported the user profiles of other similar users. There are several applications for collaborative filtering in several domains like trust and security, web services recommender systems. In the narrower sense, collaborative filters are a system by way of gathering tastes or taste knowledge from many users (collaboration), to make automatic assumptions (filtering) about a user's desires. As a co-operative filtering strategy, A is more likely to have the opinion of B on a matter if an individual A has the same opinion on a matter as a person B than a randomly-chosen participant. For example, a shared filtering recommendation program will anticipate a TV show a user wants to have a partial preference list for that user (likes or dislikes). More generally speaking, collaborator filtering is a process of filtering material or patterns through strategies involving cooperation between multiple operatives, points of view, data sources, etc. Several categories of data with the exception of: sensing and monitoring details, such as mineral exploration, environmental sensor across broad areas and several sensors; financial information, such as financial services entities which incorporate various financial sources; and online commerce and mobile application which focusses on user data, etc., have been obtained utilizing collective filtering procedures. Collaborative Filtering in Recommender System is shown in figure 2.

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ISSN- 2394-5125 VOL 7, ISSUE 10, 2020

Figure 2: Collaborative Filtering in Recommender System

II. SOCIAL NETWORK BASED RECOMMENDER SYSTEM A social network-based recommender system is defined as a social structure of individuals having a relationship supported by casual interests e.g. friendship and honesty. The social network focuses on the structure and person identification of online social networks for people who share their interests and activities or those that have an interest in browsing others’ interests and activities. These social networks, first, are employed to make friends and sharing ideas among members but today, they're employed to try to do business and data sharing. Of course, as time passes, two business and friendly environments are combined and alter into compound environments. As described above,[5] Media Scout is an application that has personalized media content via mobile devices and home TV. One of the features which exist within the system is the ability to send a request to another user within the system and to propose a friendship. The receiver of the invitation can accept or decline the invitation. If the second hand accepts the invitation, then the two users become ’friends’ of every other and may send recommendations for every other using one among the features within the application. This is often an identical scenario to the one among MSN messenger and of ICQ, apart from the usage goal. Within the two existing applications, one can chat with one’s friend and in Media Scout, a pair of friends can send movie recommendations for every other[6]. This implies that for every media item that the user watches she will be able to provide feedback on whether she liked it or not. This feedback helps the system to refine the profile of the user. For every user, the system keeps several items that she rated positively, several items that were rated negatively and a listing of friends moreover.

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Figure 3 further illustrates how these three factors impact users' final buying decisions. Intuitively, a user's buying decision or rating is determined by both his/her own preference for similar items and his/her knowledge about the characteristics of the target item. A user's preference, like Angela’s interest in Hindi movies, is typically reflected from the user's past ratings to other similar items,[7] e.g. the number of Hindi movies that Angela previously viewed and therefore, the average rating that Angela gave to those movies. Knowledge about the target item will be obtained from public media like newspapers, media, and therefore, the web. Meanwhile, the feedback from friends is another source of data regarding the item, and that they are often more trustworthy than advertisements. When a user starts considering the feedback from his/her friends, he/she is then important by his/her friends. Note that this importance isn't limited thereto of our immediate friends. Distant friends also can cast their influence indirectly to us; e.g., Angela was influenced by Linda's office mate within the previous scenario[8]. Every one of those three factors has an effect on a user’s final buying decision. If the impact from all of them is positive, it's very likely that the target user will select the item. On the contrary, if any includes a negative item rating, e.g., very low ratings in other user reviews, the possibility that the target user will select the item will decrease. With such an understanding in mind, this paper is about to propose a social network-based recommender system within the following subsections. As this paper mentioned, social influences can come from not only from immediate friends but also from relevant friends[9]. The methodology for handling these sort of influences are various relevant items. This paper shall begin with the immediate friend's importance, during which this paper only considers influences from immediate friends. Then, within the distant friend inference, this paper is going to describe how this paper incorporates influences from distant friends via leveraging the immediate friend inference.

Social Network Analysis The Social network analysis includes recording and measuring the relationships, and events among individuals, platforms and basically the other identity containing a capability of information and knowledge process. During this social network are individuals and groups, and its edges are their relationships[10]. Social network analysis includes a visible and formal analysis of human relationships. Web pages existing in it are an example of a social network. Actually, pages are often considered as nodes and links among them as edge among these nodes. On the opposite side, a replacement generation of webs appeared and considering their main factors i.e. weblogs and wikis, the importance of social networks online is now higher. Through theoretical rules, methods and related researches, social networks analysis have transformed from an implicit industry into an analytical route for paradigms. Analytical proofs evaluate all things from whole to component, structure to relations and individuals, from manner to behavior of during networks where all courses including the special relationship among the population are defined, or they consider individual networks which include courses like,[11] private societies acquired by special individuals. Traditional social network-based recommender system mostly uses user trust networks to push information to focus on groups timely and accurately. Most of those techniques are supported the collaborative filtering algorithms or their variations, requiring the systems to produce sufficient rating data. However, within the complex environment of social networks, many users regard their personal information as private, and it's difficult for recommender systems to get user trust networks and interest preferences, which leads to a dramatic reduction in recommendation efficiency. Therefore, this paper introduces a completely unique social network-based recommendation system that builds a new user relationship network by leveraging the interaction relationships among social users in complex networks. Moreover, these techniques acquire users’ preferences to the most extent to draw on all the useful social information within the social network and obtain an improved estimate of the important preferences of the target user[12]. The proposed model breaks through the inherent bottleneck within the traditional recommender systems and establishes a completely unique networking model to provide recommendations. The experiments within the section also verify that our algorithm alleviates the data sparsity problem to some extent and improves the recommendation accuracy. Social network-based recommender systems are a combination of social information on web-like user’s social networks and spatial information. Because the user’s data include personal information and interests in social networking, considering the user’s current location, and therefore, the information existing during a social network database it's possible to produce a user with an appropriate suggestion. Through this method, users’ interaction decreases, and that they can acquire their favorite information and services[13]. One valuable capability of recommender systems is applying social network. Applying social information on the web like social networks, visiting websites by user and manner of search engines to personalize content is extremely important for the user. The social network database includes problems like; movies, songs, age, job, skill and expertise,

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ISSN- 2394-5125 VOL 7, ISSUE 10, 2020 favorite food, favorite entertaining places, people can discuss their ideas about different issues, and share them with one another in these networks. For instance, if an individual has visited a special place, he/she can discuss his/her personal page and others give ideas.

III. CONCLUSION In this paper, this paper has implemented algorithms that are a collaborative filtering algorithm and an improved version of the probability method. As was expected, the improved version which is understood as a social network- based recommender system has bettered efficiency as compared with traditional collaborative filtering because, during these techniques, the user’s own preference, item’s general acceptance and influences from friends are taken into consideration. The system suggested during this paper shows the method of providing recommendations for the user, considering spatial factors and user’s personal information. During a tourist place, the suggested model is often wont to find entertainment places like the latest movies, music, books, and restaurants or art museums and concerts held in this area. Finally, introduce the social network-based recommendation system makes predictions for the item ratings. Additionally, besides the experiments, an empirical evaluation of our approach was conducted to compare too many state-of-the-art methods using Facebook data sets. However, social network recommendations supported user interaction may change significantly in several times periods. Therefore, within the near future, this paper'll specialize in the dynamic learning of user behavior that changes over time.

IV. REFERENCES

[1] Y. Afoudi, M. Lazaar, and M. Al Achhab, “Collaborative filtering recommender system,” in Advances in Intelligent Systems and Computing, 2019. [2] M. Nilashi, O. Ibrahim, and K. Bagherifard, “A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques,” Expert Syst. Appl., 2018. [3] B. Yang, Y. Lei, J. Liu, and W. Li, “Social Collaborative Filtering by Trust,” IEEE Trans. Pattern Anal. Mach. Intell., 2017. [4] D. Schall, Social Network-Based Recommender Systems. 2015. [5] Y. M. Li, C. Te Wu, and C. Y. Lai, “A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship,” Decis. Support Syst., 2013. [6] E. Q. Da Silva, C. G. Camilo-Junior, L. M. L. Pascoal, and T. C. Rosa, “An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering,” Expert Syst. Appl., 2016. [7] T. Ma et al., “Social network and tag sources based augmenting collaborative recommender system,” IEICE Transactions on Information and Systems. 2015. [8] H. Yin, Y. Sun, B. Cui, Z. Hu, and L. Chen, “LCARS: A location-content-Aware recommender system,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013. [9] L. A. Gonzalez Camacho and S. N. Alves-Souza, “Social network data to alleviate cold-start in recommender system: A systematic review,” Inf. Process. Manag., 2018. [10] W. Fan, J. Wang, Y. Ma, J. Tang, D. Yin, and Q. Li, “Deep social collaborative filtering,” in RecSys 2019 - 13th ACM Conference on Recommender Systems, 2019. [11] T. Di Noia, R. Mirizzi, V. C. Ostuni, and D. Romito, “Exploiting the web of data in model-based recommender systems,” in RecSys’12 - Proceedings of the 6th ACM Conference on Recommender Systems, 2012. [12] B. Yang, Y. Lei, D. Liu, and J. Liu, “Social collaborative filtering by trust,” in IJCAI International Joint Conference on Artificial Intelligence, 2013. [13] M. Nilashi, O. Bin Ibrahim, N. Ithnin, and N. H. Sarmin, “A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS,” Electron. Commer. Res. Appl., 2015.

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