1 IOS Press A switching multi-level method for the long tail recommendation problem a b a;∗ a Gharbi Alshammari , Jose L. Jorro-Aragoneses , Nikolaos Polatidis , Stelios Kapetanakis , c d Elias Pimenidis and Miltos Petridis a School of Computing, Engineering and Mathematics, University of Brighton, Lewes Road, BN2 4GJ, Brighton, United Kingdom E-mail: G.Alshammari, N.Polatidis, [email protected] b Department of Software Engineering and Artificial Intelligence, Universidad Complutense de Madrid, Av. Séneca, 28040, Madrid, Spain E-mail:[email protected] c Department of Computer Science and Creative Technologies, University of the West of England, Frenchay Campus, FS16 1QY, Bristol, United Kingdom E-mail:[email protected] d Department of Computer Science, Middlesex University,The Burroughs, NW4 4BT, London, United Kingdom E-mail:[email protected] Abstract. Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users’ rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail. Keywords: Recommender Systems, Collaborative Filtering, Switching, Multi-level, Long Tail Recommendations 1. Introduction and analysis using artificial intelligence approaches [1]. Recommender systems (RS) are decision support Collaborative filtering (CF) is the most successful systems well known for their use in filtering and find- technique for recommender system. Given a set of ing the relevant products on the web, thus solving the users, items and ratings, CF will suggest items to a informtion overload problem. RS can make a huge im- particular user based on common previous ratings with pact on both sides: (1) increasing the sales of a busi- other users. The main task of CF is to predict the rat- ness and (2) reduce the burden of users by finding and ing of a certain item that might meet the user inter- recommending interesting items. These recommenda- est based on common previous user ratings. The rat- tions rely on user interaction and behaviour tracking ing is the most important input in CF, which can be gathered explicitly or implicitly[2]. It works on the idea that recommending items based on the similar- *Corresponding author. E-mail:[email protected] ity between users and it was first proposed in the mid 2 G. Alshammari et al. / Switching Multi-level that solves the LTRP 1990 using the most common classification model: K- comes when the items are new to the system or have nearest neighbours (kNN). kNN main advantage of use not gained enough rating to become popular among is that it is simple to implement. Secondly, the effi- others. This issue is really essential to consider less ciency point which needs no costly steps to train the known items more than the popular one since it can model. Thus, it becomes popular among researchers add the serendipity to the users. These items are be- [3]. However, CF suffers from the long tail problem, longing to the problem of the long tail as it is intro- which affects the accuracy of the recommendations duced in [8]. Hence, those items should be considered [4]. The key issue in this technique is how to calculate and the method is able to suggest the relevant one in the similarity between users or items by finding simi- the tail. For example, the authors in [9] presented an lar shared interest. It relies on the ratings, which allow item weighting approach that filters the items in the users to assign a high or low rating to a certain item long tail and recommend them within the top ranked based on their preference or dislike for it [5]. items. Considering the importance of the long tail rec- On the other hand, content based filtering (CBF) ommendation problem, in this paper, we propose a is another recommendation technique that considers novel method that integrates the multi-level method the features of the items to find the similarity be- with the switching hybrid system. The main contribu- tween them. For example, in user terms the user pro- tions of our method are as follow: file is representing the content of the items that have – We proposed a novel recommendation method been liked/rated to reflect the user interests and pref- that applies a switching approach between CF and erences. Therefore, to make relevant recommendations CBF using a multiple-level method that improves that match against a user profile, a similarity measure the prediction accuracy when recommending the is adopted that calculates a similarity value that is close items in the long tail. to the user profile. – We examine the proposed method through a com- Many similarity measures have been adopted in rec- prehensive experiment on two public datasets us- ommender systems such as Pearson’s Correlation Co- ing two different evaluation approaches to show efficient (PCC) [2] and Cosine [6] to provide recom- the quality of the proposed method, conducting mendations based on the absolute values of the rat- a comparison with the baseline methods and a ings between users. Thus, modified similarity mea- state-of-the-art alternative. sures considered an important research area with an aim to improve the prediction accuracy. The rest of the paper is organized as follows: Sec- Regarding hybrid recommender systems, the author tion 2 contains the related work, section 3 presents the in [7] proposed a different way of using two or more proposed method, section 4 delivers the experimental techniques through seven hybridization methods in- evaluation, section 5 contains the discussion and sec- cluding: weighted, switching, mixed, feature combi- tion 6 describes the conclusions and future work parts. nation, cascade, feature augmentation, and meta-level. The main goal to combine the aforementioned meth- ods is to achieve higher quality of recommendations by 2. Related Work providing more reliable and accurate results compared to when one method is used. The authors presented A major challenge in recommender systems is to one category of the hybrid recommendation that called provide a list with high quality recommendations to EntreeC. It is a restaurant recommender system that the users. This challenge is mostly managed by first combines CBR and Collaborative filtering as a cascade finding a probability of the user to what to watch or method using the knowledge representation as a first purchase through rating prediction, then at the second step to rank the similar users based on their interest. step a ranking of the items that have a high impact Then, CF is employed among those users. follows. In the literature many works in this area fo- Many recommender systems algorithms have con- cus on the two most applied methods: CF and CBF. sidered the popular items or items with the high- CF relies on the rating similarity that is based on the est rating which are called popularity based recom- assumptions the similar users rate the similar items, mender systems. For example, in news when you read which can help predict unseen items [10]. On the other a daily news website, it will recommend you the pop- hand, CBF is based on the similarity of items fea- ular news based on the most popular news article ac- tures for example: genres or some text which repre- cording to reading frequency. However, the challenge sent the item using information retrieval and filtering G. Alshammari et al. / Switching Multi-level that solves the LTRP 3 techniques, e.g. the term Frequency-Inverse Item Fre- orative filtering and content-based filtering. In addi- quency (TF-IDF). However, the effectiveness of both tion, the authors in [17] implemented a hybrid recom- methods is limited when presented individually. Thus, mender system that applied clustering technique and hybrid recommender systema was proposed as a term an artificial algae algorithm with a multi-level CF ap- in 2002 by [7] to solve the limitation of each method by proach. However, co-rated items have been used for a using two or more methods. The recommendations are problem solving in recommender systems to improve generated to a specific user through a prediction using their predictive accuracy. Authors in [18] also intro- a similarity function that calculates how two users are duced a hybrid approach for solving the problem of similar. Then, the classification model estimates and finding the rating of unrated items in a user-item ma- identifies who is the closest one that can help calculat- trix through a weighted combination of user-based and ing the predicted value. One of the most widely used item-based collaborative filtering. These methods ad- classifier is the kNN which presents the most similar dressed the two major challenges of recommender sys- user utilizing a pre-defined number of user with simi- tems, the accuracy of the recommendations and the lar ratings which are usually refered as nearest neigh- sparsity of data, by simultaneously incorporating the borous and defined as k. In addition, the most popular correlation of users and items.