Tensor-based item recommendation using probabilistic ranking in social tagging systems

Ifada, Noor & Nayak, Richi (2014) Tensor-based item recommendation using probabilistic ranking in social tagging systems. In Chung, Chin-Wan, Broder, Andrei, Shim, Kyuseok, & Suel, Torsten (Eds.) Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, ACM, Seoul, Republic of Korea, pp. 805-810.

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Abstract

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

Impact and interest:

2 citations in Scopus
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ID Code: 72149
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Tensor model, Item recommendation, Probabilistic ranking, Folksonomy, Social tagging systems
ISBN: 9781450327459
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland
Copyright Statement: This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion http://doi.acm.org/10.1145/2567948.2579243
Deposited On: 27 May 2014 22:36
Last Modified: 27 Oct 2014 12:14

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