BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation

Yuan, F., Guo, G., Jose, J. M. , Chen, L., Yu, H. and Zhang, W. (2017) BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation. In: IUI 2017: 22nd Annual Meeting of the Intelligent User Interfaces Community, Limassol, Cyprus, 13-16 March 2017, pp. 45-54. ISBN 9781450343480 (doi:10.1145/3025171.3025211)

135914.pdf - Accepted Version



Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Chen, Dr Long and YUAN, FAJIE and Yu, Dr Haitao
Authors: Yuan, F., Guo, G., Jose, J. M., Chen, L., Yu, H., and Zhang, W.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2017 ACM
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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