In this paper, we describe our work in progress on Web services recommendation for services composition in a Mashup environment, by proposing a new approach to assist end-users based social interactions capture and analysis. This approach uses an implicit social graph inferred from the common composition interests of users. We describe the transformation of users-services interactions into a social graph and a possible means to leverage that graph to derive service recommendation. As this work is in progress, this proposal was implemented within a platform called SoCo where preliminary experiments show interesting results.
We propose a new approach for social and personalized query expansion using social structures in the Web 2.0. While focusing on social tagging systems, the proposed approach considers (i) the semantic similarity between tags composing a query, (ii) a social proximity between the query and the user profile, and (iii) on the fly, a strategy for expanding user queries. The proposed approach has been evaluated using a large dataset crawled from del.icio.us.
In this paper, we propose a framework to improve the relevance of awareness information about people and subjects, by adapting recommendation techniques to real-time web data, in order to reduce information overload. The novelty of our approach relies on the use of contextual information about people's current activities to rank social updates which they are following on Social Networking Services and other collaborative software. The two hypothesis that we are supporting in this paper are: (i) a social update shared by person X is relevant to another person Y if the current context of Y is similar to X's context at time of sharing; and (ii) in a web-browsing session, a reliable current context of a user can be processed using metadata of web documents accessed by the user. We discuss the validity of these hypothesis by analyzing their results on experimental data.
In April 2010, we conducted a survey towards 256 users of real-time microblogging platforms, mostly Twitter users, in order to analyze the usage of those platforms and evaluate the induced cognitive impact. In this article, after reporting and discussing the results of this survey, we identify opportunities for improvement towards reducing information overload and frequent disruptions. Then, we propose a novel approach for filtering status updates from real-time microblogging platforms, based on contextual relevance between their authors.
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