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Abstract-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.

Due to the significant proliferation of scholarly papers in both conferences and journals, recommending relevant papers to researchers for academic learning has become a substantial problem. Conferences, in comparison to journals have an aspect of social learning, which allows personal familiarization through various interactions among researchers. In this paper, we improve the social awareness of participants of smart conferences by proposing an innovative folksonomy-based paper recommendation algorithm, namely, Socially-Aware Recommendation of Scholarly Papers (SARSP). Our proposed algorithm recommends scholarly papers, issued by Active Participants (APs), to other Group Profile participants at the same smart conference based on similarity of their research interests. Furthermore, through computation of social ties, SARSP generates effective recommendations of scholarly papers to participants who have strong social ties with an AP. Through a relevant real-world dataset, we evaluate our proposed algorithm. Our experimental results verify that SARSP has encouraging improvements over other existing methods.

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Socially aware networking (SAN) provides a new paradigm for intermittently connected networks which exploits social properties of mobile users to guide the design of protocols. In SAN, data forwarding performance will be degraded dramatically due to the existence of users' selfish behaviors. To address the selfishness problem, barter-based incentive scheme is a fair approach in which two encounter nodes exchange the same amount of data with one another. However, it is a challenging issue for nodes to decide when two nodes contact and how many messages they will exchange for their next contacts. We consider this problem as a resource allocation problem and propose a community-based Barter incentive scheme for SAN paradigm (Com-BIS). In this method, network nodes are grouped into communities and they allocate their forwarding services for different communities optimally using 0-1 knapsack algorithm. The simulation results show that Com-BIS stimulates selfish nodes to cooperate in data delivery for other nodes effectively which improves the forwarding performance considerably.