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Subject: search.filters.subject.Social information retrieval

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    Combining Tags and Reviews to Improve Social Book Search Performance
    (Springer, 2018-08-15) Chaa, Messaoud; Nouali, Omar; Bellot, Patrice
    The emergence of Web 2.0 and social networks have provided important amounts of information that led researchers from different fields to exploit it. Social information retrieval is one of the areas that aim to use this social information to improve the information retrieval performance. This information can be textual, like tags or reviews, or non textual like ratings, number of likes, number of shares, etc. In this paper, we focus on the integration of social textual information in the research model. As it seems logical that integrating tags in the retrieval model should not be in the same way taken to integrate reviews, we will analyze the different influences of using tags and reviews on both the settings of retrieval parameters and the retrieval effectiveness. After several experiments, on the CLEF social book search collection, we concluded that combining the results obtained from two separate indexes and two models with specific parameters for tags and reviews gives good results compared to when using a single index and a single model.
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    Matching Resources in Social Environment
    (2012-06-28) Benna, Amel; Mellah, Hakima; Choui, Islam; Oualid, Ali
    User comments on the web are becoming more and more important. We focus, in this paper, on the use of user-defined tags for annotating resources to identify links between them. These links are based on a social context of the resource, obtained by applying k-means classification method and a hierarchi- cal classification of tags within a cluster. The resources are re-assigned to this classification to facilitate the search process. The ranking of results is performed according to their degree of relevance, by evaluating a similarity score between the tagged contents, in hierarchical clusters of tags, and the user request. The re- sults of the evaluation, on the social bookmarking systemdel.icio.us, demonstrate significant improvements over traditional approaches.
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    Matching Resources in Social Environment
    (CERIST, 2012-05) Benna, Amel
    User comments on web become more and more important. We focus, in this paper, on the use of user-defined tags for annotating resources to identify links between them. These links are based on a resource social context, obtained by applying k-means classification method and a hierarchical classification of tags within a cluster. The resources are re-assigned to this classification, to facilitate search process, and the ranking of result is performed according to their degree of relevance, by evaluating a similarity score between the tagged contents, in hierarchical clusters of tags, and the user request. The results of the evaluation, on social bookmarking system del.icio.us, demonstrate significant improvements over traditional approaches.
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    Building a social network, based on collaborative tagging, to enhance social information retrieval
    (CERIST, 2012) Benna, Amel; Mellah, Hakima
    Web 2.0 technologies put user at the center of data production and introduce a strong social collaboration. Therefore, the techniques used in traditional information retrieval systems do not meet the requirements of users who want to take into account their social preferences. The idea reported in this paper is to include not only the social context of the user but also that of the resource. In the social network that we consider, the user social context brings his interests, which are captured from a collaborative tagging system, while the resource social context is related to clusters of tags, obtained by classification method, and users’ opinions according to their expertise level on the resource. The results of our experiment evaluation on real-world dataset (crawled from delicious folksonomy) demonstrate significant improvements over traditional retrieval approaches.