基于注意力机制和门控网络相结合的混合推荐系统

Hybrid Recommender System Based on Attention Mechanisms and Gating Network

将用户评论和用户评分相结合来提升推荐系统的性能是推荐系统当前主流的研究方向,但是当用户评论数据稀疏时,现有的大多数推荐系统的性能会出现一定幅度的下降。针对这一问题,文中提出了一种结合注意力机制和门控网络形成的混合推荐系统(Attention Mechanism and Gating Network-based Recommendation System,AMGNRS)。该模型利用志趣相投的用户所产生的辅助评论来缓解用户评论的稀疏性问题,首先将多种混合注意力机制相结合来提高提取用户评论及评分的特征的效率,然后通过门控网络自适应地融合提取的特征并选出与用户偏好最相关的特征,最后利用神经因子分解机的高阶线性相互作用来推导评分预测。将所提模型与当前表现优异的模型在3个真实数据集上进行了对比实验,结果表明,所提模型显著地缓解了数据的稀疏性问题,验证了其有效性。

Combining user reviews with user ratings to improve the performance of recommender system is the current mainstream research direction of recommender system.However,when user review data is sparse,the performance of most existing recommender systems will degrade to a certain extent.To solve this problem,this paper proposes a hybrid recommendation system(AMGNRS),which combines attention mechanism and gating networking based recommendation system.It use auxiliary comments generated by like-minded users to alleviate the sparsity of user comments.Firstly,a variety of mixed attention mechanism are combined to impove the feature extracting efficiency of user comments and grading.Then features are extracted by adaptive fusion of gated network,and features most relevant to user preference are selected.Finally,the higher order linear interaction of the neural factorization machine is used to derive the score prediction.By comparing the model with the current model with excellent performance on three real data sets,the results show that the problem of data sparsity is significantly alleviated and the effectiveness of the model is verified.