基于DeepFM的深度兴趣因子分解机网络

推荐系统能够根据用户的喜好从海量信息中筛选出其可能感兴趣的信息并进行排序展示.随着深度学习在多个研究领域取得了良好的效果,其也开始应用于推荐系统.目前基于深度学习的推荐排序算法常采用Embedding&MLP模式,只能获得高阶的特征交互.为了解决该问题,DeepFM在上述模式中加入了因子分解机(Factorizatio...

Deep Interest Factorization Machine Network Based on DeepFM

The recommendation system can sort out and display the information that may be of interest from the mass of information according to users’preferences.As deep learning has achieved good results in multiple research fields,it has also begun to be applied to recommendation systems.However,the current recommendation ranking algorithms based on deep learning often use Embedding&MLP mode and can only obtain high-level feature interactions.In order to solve the problem that only high-order feature interaction can be obtained,DeepFM adds FM to the above mode,which can learn the low-order and high-order feature interaction end-to-end.But the DeepFM cannot express the diversity of user interests.In view of this,this paper proposes a Deep Interest Factorization Machine Network(DIFMN)by introducing the multi-head attention mechanism into DeepFM.DIFMN can adaptively learn the user representation according to the different items to be recommended,showing the diversity of user intere-sts.In addition,the model adds preference representations according to the type of user's historical behaviors,so that it can be applied not only to tasks that record only historical behaviors that the user likes,but also to tasks that record both historical beha-viors that the user likes and dislikes.This paper uses tensorflow-gpu to implement the algorithm,and performs comparative tests on two public datasets of Amazon(Electronics)and movieLen-20 m.Experiment results show that RelaImpr improves by 17.70%and 35.24%respectively compared to DeepFM,which validates the feasibility and effectiveness of the proposed method.