GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Trada: tree based ranking function adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
Detecting innovative topics based on user-interest ontology
Web Semantics: Science, Services and Agents on the World Wide Web
Transfer learning for collaborative filtering via a rating-matrix generative model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Inferring user's preferences using ontologies
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Heterogeneous cross domain ranking in latent space
Proceedings of the 18th ACM conference on Information and knowledge management
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Classical music for rock fans?: novel recommendations for expanding user interests
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A situation-aware computational trust model for selecting partners
Transactions on computational collective intelligence V
Bidirectional semi-supervised learning with graphs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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Content providers want to make recommendations across multiple interrelated domains such as music and movies. However, existing collaborative filtering methods fail to accurately identify items that may be interesting to the user but that lie in domains that the user has not accessed before. This is mainly because of the paucity of user transactions across multiple item domains. Our method is based on the observation that users who share similar items or who share social connections, can provide recommendation chains (sequences of transitively associated edges) to items in other domains. It first builds domain-specific-usergraphs (DSUGs) whose nodes, users, are linked by weighted edges that reflect user similarity. It then connects the DSUGs via the users who rated items in several domains or via the users who share social connections, to create a cross-domain-user graph (CDUG). It performs Random Walk with Restarts on the CDUG to extract user nodes that are related to the starting user node on the CDUG even though they are not present in the DSUG of the starting user node. It then adds items possessed by those users to the recommendations of the starting node user. Furthermore, to extract many more user nodes, we employ a taxonomy-based similarity measure that states that users are similar if they share the same items and/or same classes. Thus we can set many suitable routes from the starting user node to other user nodes in the CDUG. An evaluation using rating datasets in two interrelated domains and social connection histories of users as extracted from a blog portal, indicates that our method identifies potentially interesting items in other domains with higher accuracy than is possible with existing CF methods.