Improving Smart Conference Participation through Socially-Aware Recommendation
This work addresses improving conference participation for attendees through socially-aware recommendations, but it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of recommending presentation sessions at smart conferences by proposing the SARVE algorithm, which integrates social characteristics and context, and reports that it achieves more reliable and favorable results in precision, recall, and f-measure compared to two state-of-the-art methods.
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.