Paper Info
Title
Multi-relational matrix factorization using bayesian personalized ranking for social network data
Abstract
A key element of the social networks on the internet such as Facebook and Flickr is that they encourage users to create connections between themselves, other users and objects. One important task that has been approached in the literature that deals with such data is to use social graphs to predict user behavior (e.g. joining a group of interest). More specifically, we study the cold-start problem, where users only participate in some relations, which we will call social relations, but not in the relation on which the predictions are made, which we will refer to as target relations. We propose a formalization of the problem and a principled approach to it based on multi-relational factorization techniques. Furthermore, we derive a principled feature extraction scheme from the social data to extract predictors for a classifier on the target relation. Experiments conducted on real world datasets show that our approach outperforms current methods.
Year
DOI
Venue
2012
10.1145/2124295.2124317
WSDM
Keywords
Field
DocType
social network,important task,social data,principled feature extraction scheme,cold-start problem,target relation,social graph,social network data,principled approach,bayesian personalized ranking,multi-relational matrix factorization,current method,social relation,matrix factorization,recommender system,ranking,feature extraction,relational learning,cold start,recommender systems
Social relation,Data mining,Social network,Computer science,Artificial intelligence,Classifier (linguistics),The Internet,Recommender system,Ranking,Information retrieval,Matrix decomposition,Feature extraction,Machine learning
Conference
Citations 
PageRank 
References 
37
1.58
15
Authors
4
Name
Order
Citations
PageRank
Artus Krohn-Grimberghe1769.97
Lucas Drumond239524.27
Christoph Freudenthaler3185361.55
Lars Schmidt-Thieme43802216.58