Combining Tags and Reviews to Improve Social Book Search Performance

Messaoud Chaa , Omar Nouali (1) , Patrice Bellot (2, 3)

1 CERIST - Centre de recherche sur l'Information Scientifique et Technique
2 DIMAG - Data, Information & content MAnagement Group
3 LSIS - Laboratoire des Sciences de l'Information et des Systèmes

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.

Mots clés: Social Information Retrieval; Social Book Search; Docu-ment Length Normalization; User-Generated Content; Tag Based Model; Review Based Model

Domaines: Recherche d'information [cs.IR]; Traitement du texte et du document; Réseaux sociaux et d'information [cs.SI]; Web

Messaoud Chaa, Omar Nouali, Patrice Bellot. Combining Tags and Reviews to Improve Social Book Search Performance. 9th International Conference of the CLEF Association, CLEF 2018, Sep 2018, Avignon, France. pp.64-75. ⟨hal-01958654⟩