Effective hybrid recommendation combining users-searches correlations using tensors

Rawat R., Nayak R. and Li Y. (2011). Effective hybrid recommendation combining users-searches correlations using tensors. In: Web Technologies and Applications - 13th Asia-Pacific Web Conference, APWeb 2011, Proceedings. 13th Asia-Pacific Conference on Web Technology, APWeb 2011, Beijing, (131-142). April 18, 2011-April 20, 2011. doi:10.1007/978-3-642-20291-9_15


Author Rawat R.
Nayak R.
Li Y.
Title of paper Effective hybrid recommendation combining users-searches correlations using tensors
Conference name 13th Asia-Pacific Conference on Web Technology, APWeb 2011
Conference location Beijing
Conference dates April 18, 2011-April 20, 2011
Proceedings title Web Technologies and Applications - 13th Asia-Pacific Web Conference, APWeb 2011, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication Year 2011
Sub-type Fully published paper
DOI 10.1007/978-3-642-20291-9_15
ISBN 9783642202902
ISSN 0302-9743
Volume 6612 LNCS
Start page 131
End page 142
Total pages 12
Abstract/Summary 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.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Keyword association rule mining
clustering
recommendation
Tensor
web log data
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Conference Paper
Collection: Scopus Import
 
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Created: Tue, 05 Jul 2016, 12:27:13 EST by System User