Leveraging Social Connections To Improve Personalized Ranking For Collaborative Filtering

Abstract

Recommending products to users means estimating their preferences for certain items over others. This can be cast either as a problem of estimating the <i>rating</i> that each user will give to each item, or as a problem of estimating users' relative preferences in the form of a <i>ranking</i>. Although collaborative-filtering approaches can be used to identify users who rate and rank products similarly, another source of data that informs us about users' preferences is their set of <i>social connections</i>. Both rating- and ranking-based paradigms are important in real-world recommendation settings, though rankings are especially important in settings where explicit feedback in the form of a numerical rating may not be available. Although many existing works have studied how social connections can be used to build better models for <i>rating</i> prediction, few have used social connections as a means to derive more accurate <i>ranking-based</i> models. Using social connections to better estimate users' rankings of products is the task we consider in this paper. We develop a model, <i>SBPR</i> (Social Bayesian Personalized Ranking), based on the simple observation that users tend to assign higher ranks to items that their friends prefer. We perform experiments on four real-world recommendation data sets, and show that <i>SBPR</i> outperforms alternatives in ranking prediction both in warm- and cold-start settings.