Combining Tags and Reviews to Improve Social Book Search Performance

dc.contributor.authorChaa, Messaoud
dc.contributor.authorNouali, Omar
dc.contributor.authorBellot, Patrice
dc.date.accessioned2024-02-20T09:48:50Z
dc.date.available2024-02-20T09:48:50Z
dc.date.issued2018-08-15
dc.description.abstractThe 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.
dc.identifier.doihttps://doi.org/10.1007/978-3-319-98932-7_6
dc.identifier.isbn978-3-319-98931-0
dc.identifier.issn0302-9743
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/1022
dc.publisherSpringer
dc.relation.ispartofseries9th International Conference of the CLEF Association, CLEF 2018 : Proceedings; September 10-14, 2018
dc.relation.pages64-75
dc.relation.placeAvignon, France
dc.structureSécurité des systèmes et applications
dc.subjectSocial information retrieval
dc.subjectSocial book search
dc.subjectDocument length normalization
dc.subjectUser-generated content
dc.subjectTag Based Model
dc.subjectReview Based Model
dc.titleCombining Tags and Reviews to Improve Social Book Search Performance
dc.typeConference paper
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