A dual-perspective latent factor model for group-aware social event recommendation
作者：
Highlights：
• A dual-perspective of group influence on event recommendation is investigated.
• A novel probabilistic latent factor model with pairwise ranking is proposed to model the dual effect of groups.
• The proposed model is flexible to further incorporate additional contextual information including event venue, event popularity, and geographical distance.
• Comprehensive experiments are conducted to demonstrate the proposed approach yields substantial improvement over the state-of-the-art baselines on four real-world datasets in both regular and cold-start settings.
摘要
•A dual-perspective of group influence on event recommendation is investigated.•A novel probabilistic latent factor model with pairwise ranking is proposed to model the dual effect of groups.•The proposed model is flexible to further incorporate additional contextual information including event venue, event popularity, and geographical distance.•Comprehensive experiments are conducted to demonstrate the proposed approach yields substantial improvement over the state-of-the-art baselines on four real-world datasets in both regular and cold-start settings.
论文关键词：Event-based social networks,Social event recommendation,Latent factor models
论文评审过程：Received 1 July 2016, Revised 24 November 2016, Accepted 3 January 2017, Available online 24 January 2017, Version of Record 24 January 2017.
论文官网地址：https://doi.org/10.1016/j.ipm.2017.01.001
