Memory-aware gated factorization machine for top-N recommendation

Abstract

Factorization machine (FM) has recently become one of the most popular methods in collaborative filtering due to its flexibility of incorporating auxiliary information, e.g., user demographics and item genres. However, standard FM method and its deep variants (e.g., NFM and DeepFM) suffer from two key issues: (1) failing to effectively leverage user historical records, i.e., all historical records are treated equally without considering the relevance to the targeted user–item pair and (2) failing to adaptively weigh the importance of auxiliary information, i.e., auxiliary information may have negative effects on the accuracy in certain cases but existing methods cannot effectively detect and eliminate the negative effects. To this end, this paper proposes a memory-aware gated factorization machine (MAGFM), which improves the FM method by introducing two new components: (1) an external user memory matrix is introduced to each user, which can enrich the representation capacity by leveraging user historical items and the auxiliary information associated with the historical items and (2) gated filtering units are applied on top of the embedding of user/item auxiliary information, which can adaptively filter out the features with negative effects to achieve higher accuracy. Experimental studies on real-world datasets demonstrate that MAGFM can substantially outperform FM, NFM and DeepFM methods by 0.31% – 12.77% relatively in top-N recommendation.