2001 ItembasedCollaborativeFiltering

(Sarwar et al., 2001) ⇒ Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. (2001). “Item-based Collaborative Filtering Recommendation Algorithms.” In: Proceedings of the 10th International Conference on World Wide Web. ISBN:1-58113-348-0 doi:10.1145/371920.372071

Subject Headings: User-Item Preference Database.

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Abstract

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative filtering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative filtering techniques. Item-based techniques first analyze the user-item matrix to identify relationships between different items, and then use these relationships to indirectly compute recommendations for users.

In this paper we analyze different item-based recommendation generation algorithms. We look into different techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and different techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available user-based algorithms.

1. INTRODUCTION

The amount of information in the world is increasing far more quickly than our ability to process it. All of us have known the feeling of being overwhelmed by the number of new books, journal articles, and conference proceedings coming out each year. Technology has dramatically reduced the barriers to publishing and distributing information. Now it is time to create the technologies that can help us siftthrough all the available information to find that which is most valuable to us.

One of the most promising such technologies is collaborative filtering [19, 27, 14, 16]. Collaborative filtering works by building a database of preferences for items by users. A new user, Neo, is matched against the database to discover neighbors, which are other users who have historically had similar taste to Neo. Items that the neighbors like are then recommended to Neo, as he will probably also like them. Collaborative filtering has been very successful in both research and practice, and in both information filtering applications and E-commerce applications. However, there remain im- portant research questions in overcoming two fundamental challenges for collaborative filtering recommender systems. The first challenge is to improve the scalability of the col- laborative filtering algorithms. These algorithms are able to search tens of thousands of potential neighbors in real-time, but the demands of modern systems are to search tens of millions of potential neighbors. Further, existing algorithms have performance problems with individual users for whom the site has large amounts of information. For instance, if a site is using browsing patterns as indications of con- tent preference, it may have thousands of data points for its most frequent visitors. These \long user rows" slow down the number of neighbors that can be searched per second, further reducing scalability. The second challenge is to improve the quality of the rec- ommendations for the users. Users need recommendations they can trust to help them find items they will like. Users will "vote with their feet" by refusing to use recommender systems that are not consistently accurate for them. In some ways these two challenges are in con ict, since the less time an algorithm spends searching for neighbors, the more scalable it will be, and the worse its quality. For this reason, it is important to treat the two challenges simul- taneously so the solutions discovered are both useful and practical.

In this paper, we address these issues of recommender systems by applying a different approach{item-based algo- rithm. The bottleneck in conventional collaborative filtering algorithms is the search for neighbors among a large user population of potential neighbors [12]. Item-based al- gorithms avoid this bottleneck by exploring the relationships between items first, rather than the relationships between users. Recommendations for users are computed by finding items that are similar to other items the user has liked. Be- cause the relationships between items are relatively static,

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2001 ItembasedCollaborativeFiltering	George Karypis John Riedl Badrul Sarwar Joseph Konstan			Item-based Collaborative Filtering Recommendation Algorithms				10.1145/371920.372071		2001