Self-attention-based Group Recommendation

With the advent of the era of big data, the traditional recommendation system mainly for a single user, but with the society and the rapid development of e-commerce, more and more people participate in activities together, in the form of multiple users and groups of recommendation system recommended service object by a single user extensions for group members, has become a hot topic in the current society. In view of the low accuracy rate of group recommendation and the inconsistent fusion strategy among group members, the traditional solution method is matrix decomposition. MF USES a simple and fixed inner product to estimate the complex user-project interaction in low-dimensional potential space, which will cause the problem of limitation. Therefore, a group recommendation algorithm combining self-attention and NCF to solve the problem of group preference fusion is proposed. We use neural network to learn the group recommendation of fusion strategy through self-attention, and further integrate the user-project interaction improvement group recommendation through NCF model. The self-attention mechanism provided in this paper was verified on qunar and CAMRA2011 data sets. Compared with other common fusion strategies, the overall average performance of the proposed mechanism in NDCG and HR was improved.