Similarity between Euclidean and cosine angle distance for nearest neighbor queries
Proceedings of the 2004 ACM symposium on Applied computing
Higher-Order Web Link Analysis Using Multilinear Algebra
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Efficient MATLAB Computations with Sparse and Factored Tensors
SIAM Journal on Scientific Computing
Enhancing Recommendations through a Data Mining Algorithm
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
Individual and group behavior-based customer profile model for personalized product recommendation
Expert Systems with Applications: An International Journal
Application of neural networks and Kano's method to content recommendation in web personalization
Expert Systems with Applications: An International Journal
A hybrid recommendation technique based on product category attributes
Expert Systems with Applications: An International Journal
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Proceedings of the fourth ACM conference on Recommender systems
Modeling and multiway analysis of chatroom tensors
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Hi-index | 0.00 |
Most recommendation methods employ item-item similarity measures or use ratings data to generate recommendations. These methods use traditional two dimensional models to find inter relationships between alike users and products. This paper proposes a novel recommendation method using the multi-dimensional model, tensor, to group similar users based on common search behaviour, and then finding associations within such groups for making effective inter group recommendations. Web log data is multi-dimensional data. Unlike vector based methods, tensors have the ability to highly correlate and find latent relationships between such similar instances, consisting of users and searches. Non redundant rules from such associations of user-searches are then used for making recommendations to the users.