p241-herlocker - Explaining Collaborative Filtering...

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Explaining Collaborative Filtering Recommendations Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl Dept. of Computer Science and Engineering University of Minnesota Minneapolis, MN 55112 USA {herlocke, konstan, riedl}@cs.umn.edu http://www.grouplens.org/ ABSTRACT Automated collaborative filtering (ACF) systems predict a person’s affinity for items or information by connecting that person’s recorded interests with the recorded interests of a community of people and sharing ratings between like- minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems – how they should be implemented and why they should be implemented. To explore how , we present a model for explanations based on the user’s conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why , we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users. Keywords Explanations, collaborative filtering, recommender systems, MovieLens, GroupLens INTRODUCTION Automated collaborative filtering (ACF) systems predict a user’s affinity for items or information. Unlike traditional content-based information filtering system, such as those developed using information retrieval or artificial intelligence technology, filtering decisions in ACF are based on human and not machine analysis of content. Each user of an ACF system rates items that they have experienced, in order to establish a profile of interests. The ACF system then matches together that user with people of similar interests or tastes. Then ratings from those similar people are used to generate recommendations for the user. ACF has many significant advantages over traditional content-based filtering, primarily because it does not depend on error-prone machine analysis of content. The advantages include the ability to filter any type of content, e.g. text, art work, music, mutual funds; the ability to filter based on complex and hard to represent concepts, such as taste and quality; and the ability to make serendipitous recommendations. It is important to note that ACF technologies do not necessarily compete with content-based filtering. In most cases, they can be integrated to provide a powerful hybrid filtering solution. ACF systems have been successful in research, with projects
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This note was uploaded on 02/24/2010 for the course COMM 4400 at Cornell University (Engineering School).

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p241-herlocker - Explaining Collaborative Filtering...

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