Individual user behaviour modelling for effective web recommendation
Rawat, Rakesh, Nayak, Richi, & Li, Yuefeng (2011) Individual user behaviour modelling for effective web recommendation. In (Ed.) 2nd International Conference on e-Education, e-Business, e-Management and E-Learning (IC4E 2011), IEEE, Mumbai India.
With the growth of the Web, E-commerce activities are also becoming popular. Product recommendation is an effective way of marketing a product to potential customers. Based on a user’s previous searches, most recommendation methods employ two dimensional models to find relevant items. Such items are then recommended to a user. Further too many irrelevant recommendations worsen the information overload problem for a user. This happens because such models based on vectors and matrices are unable to find the latent relationships that exist between users and searches. Identifying user behaviour is a complex process, and usually involves comparing searches made by him. In most of the cases traditional vector and matrix based methods are used to find prominent features as searched by a user. In this research we employ tensors to find relevant features as searched by users. Such relevant features are then used for making recommendations. Evaluation on real datasets show the effectiveness of such recommendations over vector and matrix based methods.
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|Item Type:||Conference Paper|
|Keywords:||User Behaviour Modelling, Tensor Decomposition, Matrix Decompositions, Recommendations|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Research Centres > Smart Services CRC
|Copyright Owner:||Copyright 2011 please consult authors|
|Deposited On:||05 Dec 2011 00:54|
|Last Modified:||05 Dec 2011 00:55|
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