面向多视图融合的用户一致性社交推荐OA北大核心CSTPCD

Aiming at the problem of low accuracy of traditional social recommendation,this paper proposes a use consistent social recommendation model based on multi-view fusion.The social recommendation model takes into account the incon-sistency of users in social networks and the influence of single view information on the recommendation results.It uses the attention mechanism to dynamically filter out inconsistent social neighbors,and combines user-item interaction infor-mation to learn user feature expression.At the same time,the feature representation of the project in low-dimensional space is learned from multiple views such as knowledge graph and user-project history interaction information.Finally,the characteristics of users and items are represented by inner product operation to complete the final recommendation task.In order to verify the effectiveness of the proposed recommendation algorithm,six baseline models are compared on two public datasets of Douban and Yelp,and the recall,normalized discounted cumulative gain(NDCG)and precision are used as evaluation indicators.The experimental results show that the performance of the proposed social recommenda-tion model is better than other models.