Improving Smart Conference Participation through Socially-Aware Recommendation
Nana Yaw Asabere, Feng Xia, Wei Wang, Joel J.P.C. Rodrigues, Filippo, Basso, and Jianhua Ma

TL;DR
This paper introduces SARVE, a socially-aware recommendation algorithm that improves conference session suggestions by considering social relations and context, outperforming existing methods in accuracy and relevance.
Contribution
The paper presents SARVE, a novel socially-aware recommendation algorithm that integrates social and contextual information for personalized conference session recommendations.
Findings
SARVE outperforms CAMRS and Conference Navigator in precision, recall, and F-measure.
Incorporating social relations enhances recommendation accuracy.
Experimental results demonstrate SARVE's reliability and effectiveness.
Abstract
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,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
