Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
Unsupervised Multiway Data Analysis: A Literature Survey
IEEE Transactions on Knowledge and Data Engineering
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Tensor Decompositions and Applications
SIAM Review
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Proceedings of the fourth ACM conference on Recommender systems
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
Journal of Information Science
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Utilizing past relations and user similarities in a social matching system
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
User Modeling and User-Adapted Interaction
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Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.