An automatic learning for re-ranking in social information retrieval
Rihab Haddad, Lobna Hlaoua · 2020
A Social Information Retrieval (SIR) result re-ranking system offers several techniques and methods that meet the need of the user to express through a given query and return a re-ranked list of returned results. This list should contains the information relevant to the top ranking since the majority of these users are only interested in the first results displayed. However, optimizing the returned results ranking becomes a fundamental problem when searching for tweets. In this regard, this paper presents a re-ranking model of social retrieval results based on automatic learning. Indeed, this model can re-rank the list of results obtained by the SIR system based on adaptive features given query category. Also, our model devises queries into categories and learn the representative features of each. More precisely, in order to reorder the result obtained from a given query, our model allows recalculating a new relevance score for each corresponding tweet. To validate this proposed approach, the experiments were carried out based on queries from different categories. The results obtained from his experiments show the effectiveness of our model compared to classic ranking approaches.