融合上下文信息与自注意力机制的群组推荐算法

针对群组推荐中组内成员存在偏好动态复杂性的问题,群组成员间的偏好会彼此影响,提出了一种融合上下信息与自注意力机制的群组推荐算法。首先,为了更好地捕捉群组成员间的动态关系,利用自注意力机制自动学习群组内用户的上下文信息与群组中其他成员间的关系权重;其次,根据自注意力机制学习到的组矩阵取其均值嵌入得到群组偏好表示;最后,根据群组偏好利用贝叶斯个性化排序方法进行群组推荐。将提出的方法在MovieLens-1M和CAMRa2011两个数据集上进行实验,并且与基准算法进行比较分析,该方法在推荐的命中率HR和NDCG两个指标上均有提升,实验结果证明该方法得到了更优的群组推荐结果。

Group recommendation algorithm combining context information and self attention mechanism

There is dynamic complexity of preference among members in group recommendation, and the preferences of group members will affect each other. As for these questions, this paper proposed a group recommendation algorithm that combined the context information and self attention mechanism. Specifically, first of all, in order to better capture the dynamic relationship among group members, it used self attention mechanism to automatically learn the context information of users in the group and the relationship weights among other members in the group. Secondly, according to the group matrix learned from the self attention mechanism, it embedded the average value to obtain the group preference expression. Finally, according to group preference, it applied the Bayesian personalized ranking method into the test for group recommendation. During the research, the author used the above mentioned method to test on two datasets, namely MovieLens-1M and CAMRa2011. As a result, compared with the benchmark algorithm, this method has improved the evaluation indexes HR and NDCG. And the experimental results show that this method has achieves better effect in group recommendation.