基于双层注意力机制的深度学习电影推荐系统

传统协同过滤技术仅使用用户对物品的评分矩阵，没有充分利用用户和物品的其他多种特征，而且由于评分矩阵非常稀疏，导致推荐系统的推荐准确率严重下降。近几年深度学习技术在机器学习的多个领域取得了显著的成就，本文针对传统协同过滤推荐系统的问题，提出一种基于双层注意力机制的深度学习推荐系统。以电影推荐为例，使用深度学习框架处理推荐系统中的多种输入特征信息，同时引入双层注意力机制，分别学习用户和电影每个特征之间的偏好以及用户与其观影列表中每一部电影间的偏好，从而尽可能多地利用用户和电影的特征数据，学习用户的行为偏好，在一定程度上改善了推荐的效果。

A Deep Learning Recommendation System of Movie Based on Dual-attention Model

Traditional collaborative filtering technology only used the user’s rating matrices on items to make recommendation. Because the rating matrices were too sparse and the traditional way did not take fully advantage of the many other features of users and objects, it led to a severe drop in recommendation accuracy for recommendation systems. In recent years, deep learning technology has made remarkable achievements in many fields of machine learning, in order to improve the traditional collaborative filtering recommendation system’s situation, this paper proposed a deep learning recommendation system based on dual attention-model of movie. This system used the depth learning framework to process multiple input feature information in Recommender systems, at the same time, which introduced dual attention mechanism and used the first attention layer to learn the user’s preference for film characteristics and the second attention layer to learn user’s preference for the complete movie in their watching list. After learning the user’s preference, the experimental results show that the recommendation performance has been improved.