LIG at CLEF 2015 SBS Lab

International audienceThis paper describes the work achieved by the MRIM research group of Grenoble, using some data from the LaHC of Saint-´ Etienne, in a way to test personalized retrieval of books for the Social Book Search Lab of CLEF 2015. Our proposal rely on a biased fusion of content-only retrieval, using BM25F and LGD retrieval models, user non-social profile based on the catalog of the requester, and social profiles using user/user links generated from their catalogs and ratings on books. The official results obtained show a clear positive impact of user profile, and a small positive impact of the social elements we used. Post official results that present non biased fusion scores are also presented

LaHC at CLEF 2015 SBS Lab

International audienceThis paper describes the work of the LaHC lab of Saint-´ Etienne for the Social Book Search lab at CLEF 2015. Our goals were i) to study a field-based retrieval model (BM25F), exploiting various topics and documents fields, in order to build a strong baseline for further experiments, ii) to compare it with a Log logistic (LGD) retrieval model, and iii) to exploit some documents related to each topic (i.e. the documents given as negative or positive examples for a topic). The official results show that LGD outperforms BM25F, and that our approaches exploiting documents related to the topic requesters are based on a different interpretation of this additional information than the interpretation of the Social Book Search organizers

Word Embedding for Social Book Suggestion

International audienceThis paper presents the joint work of the Universities of Grenoble and Saint-´ Etienne at CLEF 2016 Social Book Search Suggestion Track. The approaches studied are based on personalization, considering the user's profile in the ranking process. The profile is filtered using Word Embedding, by proposing several ways to handle the generated relationships between terms. We find that tackling the problem of " non-topical " only queries is a great challenge in this case. The official results show that Word Embedding methods are able to improve results in the SBS case