Exploiting rich user information for one-class collaborative filtering

Yanen Li, Cheng Xiang Zhai, Ye Chen

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)277-301
Number of pages25
JournalKnowledge and Information Systems
Volume38
Issue number2
DOIs
StatePublished - Feb 2014

Keywords

  • Ensemble
  • One-class collaborative filtering
  • Recommender systems
  • Rich user information

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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