Effective hybrid recommendation combining users-searches correlations using tensors

Rawat, Rakesh, Nayak, Richi, & Li, Yuefeng (2011) Effective hybrid recommendation combining users-searches correlations using tensors. In Du et el, Xiaoyong (Ed.) 13th Asia-Pacific Web Conference, Springer, Beijing, China, pp. 131-142.

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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.

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ID Code: 47475
Item Type: Conference Paper
Refereed: Yes
Keywords: Tensor, Clustering, Association rule mining , Web log data, Recommendation
DOI: 10.1007/978-3-642-20291-9_15
ISBN: 978-3-642-20290-2
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Research Centres > Smart Services CRC
Copyright Owner: Copyright 2011 Springer
Copyright Statement: This is the author-version of the work.
Conference proceedings published, by Springer Verlag, will be available via SpringerLink http://www.springer.de/comp/lncs/
Deposited On: 05 Dec 2011 01:14
Last Modified: 21 Jul 2014 23:41

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