Abstract
Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.
Highlights
With the acceleration of people’s daily life, much time can be saved to get useful information quickly in a practical way, and recommender systems play a critical role in filtering information
On the Yelp dataset, as we focus on recommending topN items for each user, we use two widely adopted ranking based metrics: hit ratio (HR) and normalized discounted cumulative gain (NDCG)
Masked graph convolutional networks (GCN) propagates partial attributes instead of the entire ones via a mask vector learned for each node, and the Masked GCN significantly improves the performances compared to GCN and graph attention networks (GAT)
Summary
With the acceleration of people’s daily life, much time can be saved to get useful information quickly in a practical way, and recommender systems play a critical role in filtering information. Collaborative filtering recommendation [1] is the mainstream traditional recommendation algorithm. E collaborative filtering recommendation combined with the neural network [2] (e.g., CNN, RNN, and CDAE) alleviates this problem. Taking advantage of the productive relationship in the social networks [3,4,5] can effectively solve the cold-start problem [6, 7], but there is a malicious fraud problem by distrusting users. In social networks, there are explicit or implicit relationships between most users, which influence each other’s behaviors. Not every user and connection in a social network is trustworthy. Adding a trusted network into the recommender system can effectively solve the problem of a fraud attack
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
