Socially-Aware Venue Recommendation for Conference Participants
This addresses the challenge of effective collaboration at conferences for researchers, but it is incremental as it builds on existing recommendation techniques with social and contextual enhancements.
The paper tackles the problem of helping researchers, especially juniors, attend relevant presentation sessions at academic conferences by proposing SARVE, a socially-aware venue recommendation algorithm that uses Pearson Correlation and social characteristics, and it outperforms state-of-the-art methods in evaluations on a real-world dataset.
Current research environments are witnessing high enormities of presentations occurring in different sessions at academic conferences. This situation makes it difficult for researchers (especially juniors) to attend the right presentation session(s) for effective collaboration. In this paper, we propose an innovative venue recommendation algorithm to enhance smart conference participation. Our proposed algorithm, Social Aware Recommendation of Venues and Environments (SARVE), computes the Pearson Correlation and social characteristic information of conference participants. SARVE further incorporates the current context of both the smart conference community and participants in order to model a recommendation process using distributed community detection. Through the integration of the above computations and techniques, we are able to recommend presentation sessions of active participant presenters that may be of high interest to a particular participant. We evaluate SARVE using a real world dataset. Our experimental results demonstrate that SARVE outperforms other state-of-the-art methods.