Bilateral knowledge graph enhanced online course recommendation

作者：

Highlights：

• A personalized KG-enhanced model is proposed for online course recommendation.

• An attribute-level attention network is used in course modeling.

• The user graph is constructed to find users with similar static features.

• The interactions of similar users are used to simulate the preference of a new user.

• The model can effectively alleviate data sparsity and cold start problems.

摘要

•A personalized KG-enhanced model is proposed for online course recommendation.•An attribute-level attention network is used in course modeling.•The user graph is constructed to find users with similar static features.•The interactions of similar users are used to simulate the preference of a new user.•The model can effectively alleviate data sparsity and cold start problems.

论文关键词：Recommender system,Online course recommendation,Knowledge graph,Cold start,Personalized recommendation

论文评审过程：Received 25 June 2021, Revised 9 January 2022, Accepted 6 February 2022, Available online 9 February 2022, Version of Record 16 February 2022.

论文官网地址：https://doi.org/10.1016/j.is.2022.102000