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Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

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arxiv 1708.04617 v1 pith:NZ7ILVX6 submitted 2017-08-15 cs.LG

Jun Xiao , Hao Ye , Xiangnan He , Hanwang Zhang , Fei Wu , Tat-Seng Chua This is my paper
classification cs.LG
keywords featureinteractionsfactorizationattentionallearningmodelattentiondeep
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a $8.6\%$ relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep and DeepCross with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github.com/hexiangnan/attentional_factorization_machine

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