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Exploiting rich user information for one-class collaborative filtering

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Abstract

One-class collaborative filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed. Compared with the traditional collaborative filtering setting where the data have ratings, OCCF is more realistic in many scenarios when no ratings are available. In this paper, we propose to improve OCCF accuracy by exploiting the rich user information that is often naturally available in community-based interactive information systems, including a user’s search query history, and purchasing and browsing activities. We propose two major strategies to incorporate such user information into the OCCF models: One is to linearly combine scores from different sources, and the other is to embed user information into collaborative filtering. Furthermore, we employ the MapReduce framework for similarity computation over millions of users and items. Experimental results on two large-scale retail datasets from a major e-commerce company show that the proposed methods are effective and can improve the performance of the OCCF over baseline methods through leveraging rich user information.

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Acknowledgments

This paper is based upon work supported in part by the National Science Foundation under grant CNS-0834709.

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Authors and Affiliations

  1. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Yanen Li & ChengXiang Zhai

  2. Microsoft Corporation, 1065 La Avenida, Mountain View, CA, USA

    Ye Chen

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  1. Yanen Li
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  2. ChengXiang Zhai
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  3. Ye Chen
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Correspondence to Yanen Li.

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Li, Y., Zhai, C. & Chen, Y. Exploiting rich user information for one-class collaborative filtering. Knowl Inf Syst 38, 277–301 (2014). https://doi.org/10.1007/s10115-012-0583-9

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  • DOI: https://doi.org/10.1007/s10115-012-0583-9

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