LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates

Yuan, F., Guo, G., Jose, J. M. , Chen, L., Yu, H. and Zhang, W. (2016) LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates. In: 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, IN, USA, 24-28 Oct 2016, pp. 227-236. ISBN 9781450340731 (doi:10.1145/2983323.2983758)

122374.pdf - Accepted Version



State-of-the-art item recommendation algorithms, which apply Factorization Machines (FM) as a scoring function and pairwise ranking loss as a trainer (PRFM for short), have been recently investigated for the implicit feedback based context-aware recommendation problem (IFCAR). However, good recommenders particularly emphasize on the accuracy near the top of the ranked list, and typical pairwise loss functions might not match well with such a requirement. In this paper, we demonstrate, both theoretically and empirically, PRFM models usually lead to non-optimal item recommendation results due to such a mismatch. Inspired by the success of LambdaRank, we introduce Lambda Factorization Machines (LambdaFM), which is particularly intended for optimizing ranking performance for IFCAR. We also point out that the original lambda function suffers from the issue of expensive computational complexity in such settings due to a large amount of unobserved feedback. Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective. Further, we prove that the proposed lambda surrogates are generic and applicable to a large set of pairwise ranking loss functions. Experimental results demonstrate LambdaFM significantly outperforms state-of-the-art algorithms on three real-world datasets in terms of four standard ranking measures.

Item Type:Conference Proceedings
Additional Information:Fajie thanks the CSC funding for supporting the research. This work is also supported by the National Natural Science Foundation of China under Grant No. 61402097.
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Chen, Dr Long 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 © 2016 ACM
First Published:First published in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management: 227-236
Publisher Policy:Reproduced in accordance with the publisher copyright policy
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record