Graph neural networks recommendation model based on trust transfer and item similarity
To address the existing recommendation studies based on graph neural networks that ignore the transfer characteristics of trust relationships when using users' trust networks and the existence of inadequate utilization of the implicit relationships of graph nodes,a graph neural networks recommendation model based on trust transfer and item similarity is proposed.This model includes two aspects of node potential feature representation as well as rating prediction.The feature learning of user nodes is mainly based on the transfer characteristic of trust relationship,and the rating information of first-order trust and second-order trust of target users is obtained and aggregated using graph neural networks for user nodes.The feature learning of item nodes first mines the item similarity relationship implied in the user-item rating matrix,and then applies the graph neural networks to the item nodes for aggregation operation.Rating prediction predicts users' ratings of unrated items by aggregating node potential feature representations of users and items.Finally,the effectiveness of the model to achieve rating prediction is demonstrated by conducting experiments on the two real world datasets.
NETL
NSTL
万方数据
