2SRM: Learning social signals for predicting relevant search results

What is it about?

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.

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Photo by George Pagan III on Unsplash

Photo by George Pagan III on Unsplash

Why is it important?

This paper proposes a search model exploiting social signals. These signals (User Generated Content), collected from several social networks, can quantify some social properties such as popularity, reputation and freshness. The proposed model combines linearly two relevance scores: (1) topical, estimated using classical IR model and (2) social, estimated using some social features, popularity, reputation and the freshness of resources. Experimental evaluation conducted on two INEX datasets IMDb and SBS shows that the integration of social signals and their properties within a textual search model allows to improve the quality of the search results. Our evaluations using attributes selection algorithms and three state-of-the-art learning algorithms support our hypothesis: the rankers based on the social signals, including both the popularity, the freshness and the reputation outperform those built by using only basic textual features. We found that J48 brings the best improvement in terms of effectiveness compared to baseline and all our other proposed configurations. Analyzing ranking correlations, we note that all social signals present a positive correlation. Meanwhile, this correlation agreement justifies the significant improvement for our proposed social approach.

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