A long and short-term sequence recommendation algorithm based on knowledge graph
Existing partial sequence recommendation algorithms pay less attention to the problem of user's short-term interest over time,which leads to less-than-ideal recommendation accuracy and to-be-improved interpretability of user's interest shift.Therefore,a long-short-term sequence recommendation algorithm based on knowledge graph,namely KGLSR,is proposed.After dividing the interaction history into long and short-term behavioral sequences,it combines the convolutional neural network(CNN)with the attention mechanism for feature reconstruction of long-term interests,and introduces the knowledge graph with GAT to update user's short-term preferences.Thus,the adaptive aggregation is realized.The comparison experiments demonstrate that the proposed model outperforms the mainstream baseline model in terms of evaluation metrics of HR,MRR and NDCG,on datasets of 3 types of real scenarios.
万方数据
维普
