HGGC: A hybrid group recommendation model considering group cohesion

作者：

Highlights：

• Group recommendation model GGC considering group cohesion is proposed.

• Applying the group cohesion in group recommendation is the first attempt.

• To alleviate the data sparsity problem, HGGC integrates content information to GGC.

• Experiments are performed for three datasets, Meetup CA, Meetup NYC and Gowalla.

• Hit ratio (HR@10) of HGGC are increased by 220%, 85% and 10% over previous methods.

摘要

•Group recommendation model GGC considering group cohesion is proposed.•Applying the group cohesion in group recommendation is the first attempt.•To alleviate the data sparsity problem, HGGC integrates content information to GGC.•Experiments are performed for three datasets, Meetup CA, Meetup NYC and Gowalla.•Hit ratio (HR@10) of HGGC are increased by 220%, 85% and 10% over previous methods.

论文关键词：Group recommendation,Collaborative filtering,Topic model

论文评审过程：Received 19 September 2018, Revised 13 May 2019, Accepted 30 May 2019, Available online 7 June 2019, Version of Record 21 June 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.05.054