2SRM: Learning social signals for predicting relevant search results

Search systems based on both professional meta-data (e.g., title, description, etc.) and social signals (e.g., like, comment , rating, etc.) from social networks is the trending topic in information retrieval (IR) field. This paper presents 2SRM (Social Signals Relevance Model), an approach of IR which takes into account social signals (users' actions) as an additional information to enhance a search. We hypothesize that these signals can play a role to estimate a priori social importance (relevance) of the resource (document). In this paper, we first study the impact of each such signal on retrieval performance. Next, some social properties such as popularity, reputation and freshness are quantified using several signals. The 2SRM combines the social relevance, estimated from these social signals and properties, with the conventional textual relevance. Finally, we investigate the effect of the social signals on the retrieval effectiveness using state-of-the-art learning approaches. In order to identify the most effective signals, we adopt feature selection algorithms and the correlation between the signals. We evaluated the effectiveness of our approach on both IMDb (Internet Movie Databese) and SBS (Social Book Search) datasets containing movies and books resources and their social characteristics collected from several social networks. Our experimental results are statistically significant, and reveal that incorporating social signals in retrieval model is a promising approach for improving the retrieval performance.