News Recommendation Integrating Knowledge Graph and Users'Long-Term and Short-Term Interests
Aiming at the problem that the existing research on news recommendation systems has ignored the use of external knowledge enti-ties to mine the potential knowledge level relationships between news,and has not combined users' short-term preferences for news recommen-dation,this paper proposes a news recommendation algorithm that combines knowledge graphs and users long-term and short-term interests.The model consists of three parts:a news semantic encoder,a user interest encoder and a click predictor.In the news semantic encoder,in ad-dition to using the news's own title,introduction,and category information to learn the news semantic representation,it also uses the news ti-tle and the knowledge entities mentioned in the introduction are combined with the WikiData knowledge graph to construct a knowledge sub-graph,and learn the potential knowledge-level connections between news from the knowledge subgraph.In the user interest encoder,the at-tention mechanism is used to extract the user's long-term interest from the user's historical click news sequence,and the GRU network is used to learn the user's short-term preference,and then the user's long-term interest and short-term preference are combined to construct the us-er's comprehensive interest representation.Comparative experiments and ablation experiments were carried out on the MIND-small dataset,the KGLS model improved by 2.92%compared with the most advanced baseline model on the AUC index reflecting the accuracy of the model.
recommendation systemnews recommendationknowledge graphshort-term and long-term interestsGRU network
国家科技期刊平台
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