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.

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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.

Impact and interest:

3 citations in Scopus
2 citations in Web of Science®
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39 since deposited on 10 Nov 2014
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ID Code: 78102
Item Type: Journal Article
Refereed: Yes
Additional Information: Special issue for the 22nd International Conference, UMAP 2014, Aalborg, Denmark, July 7-11, 2014.
Keywords: tensor reconstruction, probabilistic ranking, item recommendation
DOI: 10.1007/978-3-319-08786-3_9
ISBN: 978-3-319-08786-3
ISSN: 1611-3349
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000)
Divisions: 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: 23 Jun 2017 14:28

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