Enhanced graph recommendation with heterogeneous auxiliary information

Abstract

The boom in the field of movies and TV programs, which is a kind of information overload, may lead to poor user experience and are detrimental to the healthy development of the industry, hence personalized program recommendation is crucial. Since program names, labels, and synopsis are highly condensed languages, to enable better semantic representations for personalized recommendations and enrich the completeness requirements of data resources, we propose an enhanced graph recommendation with heterogeneous auxiliary information (EGR-HA), focusing on auxiliary information knowledge representations, and graph neural network-based node updates. Firstly, multi-source heterogeneous auxiliary information knowledge is fused to supplement semantics of program and user to obtain initial representations that contain rich semantics, then user and program node embedding representations are aggregated in multiple layers through graph neural networks to model higher-order interaction history information and realize user and program representation update; finally, user viewing prediction is performed based on deep networks to realize personalized program recommendation. The final experiment results in indicators, such as normalized discounted cumulative gain (NDCG), hit rate (HR) and root mean square error (RMSE), verified the effectiveness of this method by comparing with various methods.