Probabilistic Ensemble of Collaborative Filters

Authors

  • Zhiyu Min Alibaba Group
  • Dahua Lin The Chinese University of Hong Kong

DOI:

https://doi.org/10.1609/aaai.v32i1.11898

Keywords:

Recommendation Systems, Ensemble

Abstract

Collaborative filtering is an important technique for recommendation. Whereas it has been repeatedly shown to be effective in previous work,its performance remains unsatisfactory in many real-world applications, especially those where the items or users are highly diverse. In this paper, we explore an ensemble-based framework to enhance thecapability of a recommender in handling diverse data. Specifically, we formulate a probabilistic model which integrates the items, the users, as well as the associations between them into a generative process. On top of this formulation, we further derive a progressive algorithm to construct an ensemble of collaborative filters. In each iteration, a new filter is derived from re-weighted entries and incorporated into the ensemble. It is noteworthy that while the algorithmic procedure of our algorithm is apparently similar to boosting, it is derived from an essentially different formulation and thus differs in several key technical aspects. We tested the proposed method on three large datasets, and observed substantial improvement over the state of the art, including L2Boost, an effective method based on boosting.

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Published

2018-04-26

How to Cite

Min, Z., & Lin, D. (2018). Probabilistic Ensemble of Collaborative Filters. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11898

Issue

Section

Main Track: Machine Learning Applications