基于双层注意力机制的联合深度推荐模型

Joint Deep Recommendation Model Based on Double-Layer Attention Mechanism

许多电子商务网站中存在用户编写的大量评论信息,大部分推荐系统虽然利用了评论信息,但仅从单词级别而不是评论级别来评估评论的重要性。如果只考虑评论中的重要单词,而忽略了真正有用的评论,则会降低推荐模型的性能。基于此,文中提出了一种基于双层注意力机制的联合深度推荐模型(DLALSTM)。该模型首先利用双向长短期记忆网络(BiLSTM)分别对用户和项目评论进行词以及评论级别联合建模,并通过两层注意力机制聚合为评论表示和用户/项目表示,然后把从评论中学习的用户和项目的潜在表示融入由评分矩阵得到的用户偏好和项目特征,实现评分预测。采用文中模型在Yelp和亚马逊的不同领域数据集上进行实验评估,并与常用的推荐方法进行比较,发现文中提出的模型性能超过目前常用的推荐方法,同时该模型能够缓解数据稀疏问题,且具有较好的可解释性。

Many e-commerce websites keep a large amount of customer's reviews.The reviews was exploited by most recommendation systems by only considering their importance in the word-level rather than in the comment-level.The effectiveness of the recommendation model will be reduced by exclusively considering important words in the reviews and ignoring really useful reviews.Based on this,a joint deep recommendation model based on double-layer attention mechanism(DLALSTM)was proposed.First,DLALSTM uses bidirectional long short-term memory network(BiLSTM)to jointly model the customer and reviews from both word and customer levels,and aggregates the reviews representation and the customer/item representation by a double-layer attention mechanism.Then,the latent representation of customer and item learned from the reviews was incorporated into the customer preference and item feature obtained from the rating matrix to make rating prediction.DLALSTM was compared with the commonly used recommendation methods through experimental evaluation on different domain datasets of Yelp and Ama-zon.It finds that the performance of DLALSTM exceeds the state of art recommended methods.Meanwhile,the proposed model can alleviate the sparsity problem to some extent and has good interpretability.