A collaborative filtering recommendation algorithm based on DeepWalk and self-attention

Graph embedding is one of the vital technologies in solving the problem of information overload in recommendation systems. It can simplify the vector representations of items and accelerates the calculation process. Unfortunately, the recommendation system using graph embedding technology does not consider the deep relationships between items when it learns embedding vectors. In order to solve this problem, we propose a collaborative filtering recommendation algorithm based on DeepWalk and self-attention in this paper. This algorithm can enhance the accuracy in measuring the similarity between items and obtain more accurate embedding vectors. Chronological order and mutual information are used to construct a weighted directed relationship graph. Self-attention and DeepWalk are utilised to generate embedding vectors. Then item-based collaborative filtering is utilised to obtain recommended lists. The result of the relative experiments and evaluations on three public datasets shows that our algorithm is better than the existing ones.