Towards social-aware interesting place finding in social sensing applications

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This paper develops a principled approach to accurately identify interesting places in a city through social sensing applications. Social sensing has emerged as a new application paradigm, where a crowd of social sources (humans or devices on their behalf) collectively contribute a large amount of observations about the physical world. This paper studies an interesting place finding problem, in which the goal is to correctly identify the interesting places in a city. Important challenges exist in solving this problem: (i) the interestingness of a place is not only related to the number of users who visit it, but also depends upon the travel experience of the visiting users; (ii) the user’s social connections could directly affect their visiting behavior and the interestingness judgment of a given place. In this paper, we develop a new Social-aware Interesting Place Finding Plus (SIPF+) approach that addresses the above challenges by explicitly incorporating both the user’s travel experience and social relationship into a rigorous analytical framework. The SIPF+ scheme can find interesting places not typically identified by traditional travel websites (e.g., TripAdvisor, Expedia). We compare our solution with state-of-the-art baselines using two real-world datasets collected from location-based social network services and verified the effectiveness of our approach.

论文关键词:Interesting place finding,Social dependency,Social sensing,Crowdsourcing,Expectation maximization

论文评审过程:Received 23 July 2016, Revised 27 January 2017, Accepted 1 February 2017, Available online 14 February 2017, Version of Record 27 March 2017.

论文官网地址:https://doi.org/10.1016/j.knosys.2017.02.006