Improving Smart Conference Participation through Socially-Aware Recommendation

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, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.