Optimizing Factorization Machines for Top-N Context-aware Recommendations

Yuan, F., Guo, G., Jose, J. M. , Chen, L., Yu, H. and Zhang, W. (2016) Optimizing Factorization Machines for Top-N Context-aware Recommendations. In: 17th International Conference on Web Information Systems Engineering (WISE 2016), Shanghai, China, 7-10 Nov 2016, pp. 278-293. ISBN 9783319487397 (doi:10.1007/978-3-319-48740-3_20)

123463.pdf - Accepted Version



Context-aware Collaborative Filtering (CF) techniques such as Factorization Machines (FM) have been proven to yield high precision for rating prediction. However, the goal of recommender systems is often referred to as a top-N item recommendation task, and item ranking is a better formulation for the recommendation problem. In this paper, we present two collaborative rankers, namely, Ranking Factorization Machines (RankingFM) and Lambda Factorization Machines (LambdaFM), which optimize the FM model for the item recommendation task. Specifically, instead of fitting the preference of individual items, we first propose a RankingFM algorithm that applies the cross-entropy loss function to the FM model to estimate the pairwise preference between individual item pairs. Second, by considering the ranking bias in item recommendations, we design two effective lambda-motivated sampling schemes to optimize desired ranking metrics. The models we propose can work with any types of context, and are capable of estimating latent interactions between the context features under sparsity. Experimental results demonstrate its superiority over several state-of-the-art methods on three real-world CF datasets in terms of two standard ranking metrics

Item Type:Conference Proceedings
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 Springer International Publishing AG
First Published:First published in Lecture Notes in Computer Science 10041: 278-293
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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