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A Two-Stage Item Recommendation Method Using Probabilistic Ranking with Reconstructed Tensor Model

  • Noor Ifada
  • Richi Nayak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8538)

Abstract

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 n-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.

Keywords

tensor reconstruction probabilistic ranking item recommendation 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Noor Ifada
    • 1
  • Richi Nayak
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia

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