Paper Info
Title
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 rating that each user will give to each item, or as a problem of estimating users' relative preferences in the form of a ranking. 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 social connections. 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 rating prediction, few have used social connections as a means to derive more accurate ranking-based models. Using social connections to better estimate users' rankings of products is the task we consider in this paper. We develop a model, SBPR (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 SBPR outperforms alternatives in ranking prediction both in warm- and cold-start settings.
Year
DOI
Venue
2014
10.1145/2661829.2661998
CIKM
Keywords
Field
DocType
data mining,personalized ranking,recommender systems,social networks
Recommender system,Data mining,Data set,Social network,Collaborative filtering,Information retrieval,Ranking,Computer science,Bayesian probability
Conference
Citations 
PageRank 
References 
102
2.42
25
Authors
3
Search Limit
100102
Name
Order
Citations
PageRank
Tong Zhao122014.25
Julian John McAuley22856115.30
Irwin King36751325.94