GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
Recommendations in taste related domains: collaborative filtering vs. social filtering
Proceedings of the 2007 international ACM conference on Supporting group work
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Tag recommendations in social bookmarking systems
AI Communications
Scalable Tensor Decompositions for Multi-aspect Data Mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Tagcloud-based explanation with feedback for recommender systems
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Making recommendations from multiple domains
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction of atomic web services reliability based on k-means clustering
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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Social network systems such as FaceBook and YouTube have played a significant role in capturing both explicit and implicit user preferences for different items in the form of ratings and tags. This forms a quaternary relationship among users, items, tags and ratings. Existing systems have utilized only ternary relationships such as users-items-ratings, or users-items-tags to derive their recommendations. In this paper, we show that ternary relationships are insufficient to provide accurate recommendations. Instead, we model the quaternary relationship among users, items, tags and ratings as a 4-order tensor and cast the recommendation problem as a multi-way latent semantic analysis problem. A unified framework for user recommendation, item recommendation, tag recommendation and item rating prediction is proposed. The results of extensive experiments performed on a real world dataset demonstrate that our unified framework outperforms the state-of-the-art techniques in all the four recommendation tasks.