GIST: A generative model with individual and subgroup-based topics for group recommendation

作者：

Highlights：

• A novel Topic-based model is proposed to make group recommendations.

• A new type of subgroup topic is introduced to enrich group activity analysis.

• A new solution to choice aggregation is designed to inferring group decision.

• The link information of group members is used to optimize the weights.

• Results on real-life data are presented to illustrate the performance of our model.

摘要

•A novel Topic-based model is proposed to make group recommendations.•A new type of subgroup topic is introduced to enrich group activity analysis.•A new solution to choice aggregation is designed to inferring group decision.•The link information of group members is used to optimize the weights.•Results on real-life data are presented to illustrate the performance of our model.

论文关键词：Group recommendation,Group activity,Decision making,Topic model,Recommender systems

论文评审过程：Received 23 June 2017, Revised 14 October 2017, Accepted 15 October 2017, Available online 20 October 2017, Version of Record 5 November 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.10.037