A dual-perspective latent factor model for group-aware social event recommendation. Issue 3 (May 2017)

• Record Type: Journal Article
• Authors: Jhamb, Yogesh; Fang, Yi
• Is Part Of: Information processing & management. Volume 53:Issue 3(2017:May)
• Page Start: 559
• Page End: 576
• Publication Date: 2017-05
• Subjects: Event-based social networks -- Social event recommendation -- Latent factor models; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2017.01.001
• Languages: English
• ISSNs: 0306-4573

Abstract

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. Abstract: Event-based social networks (EBSNs) have experienced increased popularity and rapid growth. Due to the huge volume of events available in EBSNs, event recommendation becomes essential for users to find suitable events to attend. Different from classic recommendation scenarios (e.g., movies and books), a large majority of EBSN users join groups unified by a common interest, and events are organized by groups. In this paper, we propose a dual-perspective latent factor model for group-aware event recommendation by using two kinds of latent factors to model the dual effect of groups: one from the user-oriented perspective (e.g., topics of interest) and another from the event-oriented perspective (e.g., event planning and organization). Pairwise learning is used to exploit unobserved RSVPs by modeling the individual probability of preference via Logistic and Probit sigmoid functions. These latent group factors alleviate the cold-start problems, which are pervasive in event recommendation because events published in EBSNs are always in the future and many of them have little or no trace of historical attendance. The proposed model is flexible and we further incorporate additional contextual information such as event venue, event popularity, temporal influence and geographical distance. We conduct a comprehensive set of experiments on four datasets from Meetup in both regular and cold-start settings. The results demonstrate that the proposed approach yields substantial improvement over the state-of-the-art baselines by utilizing the dual latent factors of groups.