2 research outputs found
Social- and Mobility-Aware Device-to-Device Content Delivery
Mobile online social network services have seen a rapid increase, in which
the huge amount of user-generated social media contents propagating between
users via social connections has significantly challenged the traditional
content delivery paradigm: First, replicating all of the contents generated by
users to edge servers that well "fit" the receivers becomes difficult due to
the limited bandwidth and storage capacities. Motivated by device-to-device
(D2D) communication that allows users with smart devices to transfer content
directly, we propose replicating bandwidth-intensive social contents in a
device-to-device manner. Based on large-scale measurement studies on social
content propagation and user mobility patterns in edge-network regions, we
observe that (1) Device-to-device replication can significantly help users
download social contents from nearby neighboring peers; (2) Both social
propagation and mobility patterns affect how contents should be replicated; (3)
The replication strategies depend on regional characteristics ({\em e.g.}, how
users move across regions).
Using these measurement insights, we propose a joint \emph{propagation- and
mobility-aware} content replication strategy for edge-network regions, in which
social contents are assigned to users in edge-network regions according to a
joint consideration of social graph, content propagation and user mobility. We
formulate the replication scheduling as an optimization problem and design
distributed algorithm only using historical, local and partial information to
solve it. Trace-driven experiments further verify the superiority of our
proposal: compared with conventional pure movement-based and popularity-based
approach, our design can significantly ( times) improve the amount of
social contents successfully delivered by device-to-device replication
Latent Networks Fusion based Model for Event Recommendation in Offline Ephemeral Social Networks
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.Comment: Full version of ACM CIKM2013 pape