Latent Networks Fusion based Model for Event Recommendation in Offline Ephemeral Social Networks

TL;DR

This paper introduces a novel Latent Networks Fusion model for event recommendation in offline ephemeral social networks, leveraging heterogeneous interaction networks to infer user preferences and social relations without explicit ratings.

Contribution

The paper proposes a unified model that fuses latent preferences and social relations derived from heterogeneous networks for improved event recommendation in OffESNs.

Findings

The LNF model outperforms traditional methods on RFID-based conference data.

Latent social relations improve recommendation accuracy.

Fusion of multiple networks enhances user preference modeling.

Abstract

With the growing amount of mobile social media, offline ephemeral social networks (OffESNs) are receiving more and more attentions. Offline ephemeral social networks (OffESNs) are the networks created ad-hoc at a specific location for a specific purpose and lasting for short period of time, relying on mobile social media such as Radio Frequency Identification (RFID) and Bluetooth devices. The primary purpose of people in the OffESNs is to acquire and share information via attending prescheduled events. Event Recommendation over this kind of networks can facilitate attendees on selecting the prescheduled events and organizers on making resource planning. However, because of lack of users preference and rating information, as well as explicit social relations, both rating based traditional recommendation methods and social-trust based recommendation methods can no longer work well to recommend events in the OffESNs. To address the challenges such as how to derive users latent preferences and social relations and how to fuse the latent information in a unified model, we first construct two heterogeneous interaction social networks, an event participation network and a physical proximity network. Then, we use them to derive users latent preferences and latent networks on social relations, including like-minded peers, co-attendees and friends. Finally, we propose an LNF (Latent Networks Fusion) model under a pairwise factor graph to infer event attendance probabilities for recommendation. Experiments on an RFID-based real conference dataset have demonstrated the effectiveness of the proposed model compared with typical solutions.