A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model
Ifada, Noor & Nayak, Richi (2014) A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model. Lecture Notes in Computer Science : User Modeling, Adaptation, and Personalization, 8538, pp. 98-110.
In a tag-based recommender system, the multi-dimensional <user, item, tag> correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.
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|Item Type:||Journal Article|
|Additional Information:||Special issue for the 22nd International Conference, UMAP 2014, Aalborg, Denmark, July 7-11, 2014.|
|Keywords:||tensor reconstruction, probabilistic ranking, item recommendation|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 Springer International Publishing Switzerland|
|Deposited On:||10 Nov 2014 01:05|
|Last Modified:||07 Dec 2015 22:20|
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