Comparison of Recommender System Algorithms Focusing on the New-item and User-bias Problem
Recommender systems are used by an increasing number of e-commerce websites to help the customers to find suitable products from a large database. One of the most popular techniques for recommender systems is collaborative filtering. Several collaborative
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Department of Computer Science, University of Freiburg Georges-Koehler-Allee 51, 79110 Freiburg, Germany [email protected] Information Systems and Machine Learning Lab, University of Hildesheim Samelsonplatz 1, 31141 Hildesheim, Germany {tso,schmidt-thieme}@ismll.uni-hildesheim.de
Abstract. Recommender systems are used by an increasing number of e-commerce websites to help the customers to find suitable products from a large database. One of the most popular techniques for recommender systems is collaborative filtering. Several collaborative filtering algorithms claim to be able to solve i) the new-item problem, when a new item is introduced to the system and only a few or no ratings have been provided; and ii) the user-bias problem, when it is not possible to distinguish two items, which possess the same historical ratings from users, but different contents. However, for most algorithms, evaluations are not satisfying due to the lack of suitable evaluation metrics and protocols, thus, a fair comparison of the algorithms is not possible. In this paper, we introduce new methods and metrics for evaluating the user-bias and newitem problem for collaborative filtering algorithms which consider attributes. In addition, we conduct empirical analysis and compare the results of existing collaborative filtering algorithms for these two problems by using several public movie datasets on a common setting.
1 Introduction A Recommender system is a type of customization tool in e-commerce that generates personalized recommendations, which match with the taste of the users. Collaborative filtering (CF) (Sarwar et al. (2000, 2001)) is a popular technique used in recommender systems. It is used to predict the user interest for a given item based on user profiles. The concept of this technique is that the user, who received a recommendation for some sorts of items, would prefer the same items as other individuals with a similar mind set. However, besides its simplicity, one of the shortcomings of CF are the new-item or cold-start problem. If no ratings are given for new items, it is difficult for standard CF algorithms to determine their own clusters by using rating similarity and thus they fail to give accurate predictions. Another problem is the user-bias from historical ratings (Kim and Li (2004)), which occurs when two items, based on historical ratings
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Stefan Hauger, Karen H. L. Tso and Lars Schmidt-Thieme
Fig. 1. User-Bias Example
have the same opportunity to be recommended to a user, but additional information shows that one item belongs to a group which is preferred by the user and the other not. For example, as shown in Figure 1, by applying CF, the probabilities that item 4 and 5 to be recommended for user 1 are equal. When the attributes are also taken into consideration, it can be observed that items 1, 3 and 6 which belong to attribute 1 are rated higher than user 1 than item 2 which belongs to attribute 2. Thus, user 1 has a preference for items related to attribute 1 over items related to
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