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.

因子分解机（FM）最近因其合并辅助信息（例如用户人口统计和项目类型）的灵活性而成为协作过滤中最受欢迎的方法之一。但是，标准FM方法及其深层变体（例如NFM和DeepFM）存在两个关键问题：（1）无法有效利用用户历史记录，即，所有历史记录均被平等对待，而没有考虑与目标用户的相关性–项目对和（2）未能自适应地权衡辅助信息的重要性，即，在某些情况下，辅助信息可能对准确性产生负面影响，但是现有方法无法有效地检测和消除负面影响。为此，本文提出了一种内存感知门控分解机（MAGFM），它通过引入两个新组件来改进FM方法：（1）向每个用户引入一个外部用户存储矩阵，该矩阵可以通过利用用户历史项和与历史项相关联的辅助信息来丰富表示能力，以及（2）在嵌入用户/项目辅助信息的基础上应用了过滤单元，该过滤单元可以自适应地过滤出具有负面影响的特征，以实现更高的精度。对现实世界数据集的实验研究表明，MAGFM可以比FM，NFM和DeepFM方法的性能大大优于top-N推荐值0.31％– 12.77％。可以通过利用用户历史项和与历史项相关的辅助信息来丰富表示能力，并且（2）在用户/项辅助信息的嵌入之上应用了门控过滤单元，可以自适应地过滤出带有负数的特征效果达到更高的精度。对现实世界数据集的实验研究表明，MAGFM可以比FM，NFM和DeepFM方法的性能大大优于top-N推荐值0.31％– 12.77％。可以通过利用用户历史项和与历史项相关的辅助信息来丰富表示能力，并且（2）在用户/项辅助信息的嵌入之上应用了门控过滤单元，可以自适应地过滤出带有负数的特征效果达到更高的精度。对现实世界数据集的实验研究表明，MAGFM可以比FM，NFM和DeepFM方法的性能大大优于top-N推荐值0.31％– 12.77％。