关键词: 推荐系统, 双塔模型, 孪生网络, 深度学习, 卷积神经网络

Abstract: As a classic algorithm in recommender systems, two-tower neural networks have been widely used in large-scale information retrieval tasks. But because the two neural networks are independent of each other, the neural network cannot learn the interaction information between users and items. In addition, due to the lack of learning ability for graph connection information, its retrieval range is narrower than that of graph learning algorithms. To address the above problems, a new algorithm TSN(Two-stage siamese graph convolutional neural network recommendation algorithm) is proposed. First, build a heterogeneous graph based on user behavior; then, design a graph convolutional siamese network between the two-tower neural networks, so as to achieve information interaction while learning the connection information of the heterogeneous graph; finally, by designing a special structure of two-stage information sharing mechanism enables the neural networks on the user side and the item side to transmit information dynamically and bidirectionally during the training process, and effectively avoids neural network cascading. After comparative experiments based on MovieLens and Douban movie datasets, the average performance of HR@N, NDCG@N, and MRR@N is improved by 4.70% and 13.28% respectively compared with the optimal benchmark algorithm DAT. The results show that this method can alleviate the problem of lack of information interaction in the twin-tower neural network. Compared with the previous algorithm, the recommendation performance is significantly improved.

Key words: recommendation system, two-tower model, siamese network, deep learning, convolutional neural network