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Optimizing Factorization Machines for Top-N Context-aware Recommendations

By Fajie Yuan, Guibing Guo, Joemon M. Jose, Long Chen, Haitao Yu and Weinan Zhang


Context-aware Collaborative Filtering (CF) techniques such\ud as Factorization Machines (FM) have been proven to yield high precision\ud for rating prediction. However, the goal of recommender systems is often referred to as a top-N item recommendation task, and item ranking\ud is a better formulation for the recommendation problem. In this paper,\ud we present two collaborative rankers, namely, Ranking Factorization Machines (RankingFM) and Lambda Factorization Machines (LambdaFM),\ud 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\ud 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\ud to optimize desired ranking metrics. The models we propose can work\ud with any types of context, and are capable of estimating latent interactions between the context features under sparsity. Experimental results\ud demonstrate its superiority over several state-of-the-art methods on three\ud real-world CF datasets in terms of two standard ranking metric

Year: 2016
OAI identifier:
Provided by: Enlighten

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