SoNARS: A Social Networks-Based Algorithm for Social Recommender Systems

Double-Sided Recommendations: A Novel Framework for Recommender Systems

“…Taking inspiration from past work on exploiting social influence dynamics in the recommendation process [5] and, in particular, from social comparison theory 1 , this method suggests items that were positively evaluated, on average, by relevant others. User relevance depends on both user similarity and user affiliations (i.e., relationship strength), based on the idea that close contacts are more likely to exert some influence.…”

“…Guy et al [8] found that users prefer recommendations generated taking into account their social network with respect to recommendations based on user-user similarity, as in collaborative filtering, especially when explanations are provided which highlight which people are related to each recommended item. Carmagnola et al [5] claimed that the mere fact of being part of a social network may cause individuals to modify their attitudes and behaviours because of social influence dynamics, and proposed SoNARS, a recommendation algorithm which explicitly targets users as members of their social network.…”

“…Most of them focus on suggesting users to affiliate to existing, explicitly-defined communities, based either on structural properties of their social networks (e.g., user proximity to a community [20] or the number and relevance of friends who already belong to it [5]) or on content-related features, such as predicted user interest for the topics which are usually associated to such communities [2]. However, groups to recommend could also be generated on-the-fly by taking into account the social networks of the target users, for example in case a well-matched group of friends to invite to a party should be suggested.…”

A study of the dynamic features of recommender systems

“…A number of evolutionary algorithm as well as other modifications of standard collaborative filtering algorithm are being continuously devised to deal with the dynamism of the whole process so far (Geyer-Schulz 2000; Demir et al 2007). The advancement in the social networks over the internet also provided a new direction to these efforts (Carmagnola et al 2009). Zhou et al (2011) conducted a review of the state of the art of the existing technologies for building personalized recommender systems in social networking environment.…”

A User-Centric Approach for Social Data Integration and Recommendation

“…One such approach is SoNARS. It takes a hybrid approach, combining results from collaborative filtering and content-based algorithms [9]. Dave Briccetti developed a Twitter desktop client application called TalkingPuffin (talkingpuffin.org).…”