  This research addresses recommending presentation sessions at smart
conferences to participants. We propose a venue recommendation algorithm,
Socially-Aware Recommendation of Venues and Environments (SARVE). SARVE
computes correlation and social characteristic information of conference
participants. In order to model a recommendation process using distributed
community detection, SARVE further integrates the current context of both the
smart conference community and participants. SARVE recommends presentation
sessions that may be of high interest to each participant. We evaluate SARVE
using a real world dataset. In our experiments, we compare SARVE to two related
state-of-the-art methods, namely: Context-Aware Mobile Recommendation Services
(CAMRS) and Conference Navigator (Recommender) Model. Our experimental results
show that in terms of the utilized evaluation metrics: precision, recall, and
f-measure, SARVE achieves more reliable and favorable social (relations and
context) recommendation results.
