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Journal Articles Proceedings of the ACM on Human-Computer Interaction Year : 2017

WeBrowse: Leveraging User Clicks for Content Discovery in Communities of a Place

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Abstract

One of the limits of web content discovery tools, let them be recommender systems or content curation tools such as social rating, social bookmarking and other social media, is the scarcity of user input (e.g. rate, submit, share). This problem is even worse in the case of what we call communities of a place: people who study, live or work at the same place. Such people often share common interests but either do not know each other or fail to actively engage in submitting and relaying information. In this paper, we investigate the feasibility of using the aggregated clicks of entire communities of users to passively emulate a content curation service a la Reddit. To this end, we prototype and deploy WeBrowse, a content curation service based on the processing of raw HTTP logs. Evaluation based on our deployments demonstrates feasibility at scale while respecting user privacy. The majority of WeBrowse's users welcome the quality of content it promotes.
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Dates and versions

hal-01663712 , version 1 (14-12-2017)

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Giuseppe Scavo, Zied Ben Houidi, Stefano Traverso, Renata Teixeira, Marco Mellia. WeBrowse: Leveraging User Clicks for Content Discovery in Communities of a Place. Proceedings of the ACM on Human-Computer Interaction , 2017, 1 (CSCW), pp.93:1-93:24. ⟨10.1145/3134728⟩. ⟨hal-01663712⟩
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