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

@inproceedings{Ifada2014TensorbasedIR,
  title={Tensor-based item recommendation using probabilistic ranking in social tagging systems},
  author={Noor Ifada and Richi Nayak},
  booktitle={WWW},
  year={2014}
}
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… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.
7 Citations
10 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 10 references

C.F.V., Matrix Computations, 4 ed

  • G. H. Golub, Loan
  • 2013
Highly Influential
3 Excerpts

Similar Papers

Loading similar papers…