Social Recommender systems: Improving recommendations through personalization

Abstract

The vision for Web 3.0 (popularly referred to as the Semantic Web) is the ability to create meaning out of a deluge of qualitative data. This paper explores a very specific instance of the Semantic Web – Social Recommender Systems. This paper discusses the possibility of converting social crowdsourced data into quantitative information and using this information to power social recommendations. Over the course of this paper, we discuss five important recommender algorithms. This paper first outlines the importance of recommendations and elaborates the different types of recommendation algorithms used widely. We then discuss the potential to further personalize these recommendations by trying to identify user ‘taste’ by capitalizing on the social data available about the user. Next, we discuss the availability and applicability of data from social networks and how this data may be processed into quantitative input. This data is then used as input for two different social algorithms and their merits are discussed. And lastly this paper covers the topic of creating hybrid systems out of a wide range of recommendation algorithms so as to create social systems which are able to give diverse and personalized recommendations.