  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.

Social and Information Networks;Physics and Society