• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Tensor-based tag emotion aware recommendation with probabilistic ranking

Vol. 13, No. 12, December 31, 2019
10.3837/tiis.2019.12.003, Download Paper (Free):

Abstract

In our previous research, we proposed a tag emotion-based item recommendation scheme. The ternary associations among users, items, and tags are described as a three-order tensor in order to capture the emotions in tags. The candidates for recommendation are created based on the latent semantics derived by a high-order singular value decomposition technique (HOSVD). However, the tensor is very sparse because the number of tagged items is smaller than the amount of all items. The previous research do not consider the previous behaviors of users and items. To mitigate the problems, in this paper, the item-based collaborative filtering scheme is used to build an extended data. We also apply the probabilistic ranking algorithm considering the user and item profiles to improve the recommendation performance. The proposed method is evaluated based on Movielens dataset, and the results show that our approach improves the performance compared to other methods.


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Cite this article

[IEEE Style]
H. Lim and H. Kim, "Tensor-based tag emotion aware recommendation with probabilistic ranking," KSII Transactions on Internet and Information Systems, vol. 13, no. 12, pp. 5826-5841, 2019. DOI: 10.3837/tiis.2019.12.003.

[ACM Style]
Hyewon Lim and Hyoung-Joo Kim. 2019. Tensor-based tag emotion aware recommendation with probabilistic ranking. KSII Transactions on Internet and Information Systems, 13, 12, (2019), 5826-5841. DOI: 10.3837/tiis.2019.12.003.

[BibTeX Style]
@article{tiis:23083, title="Tensor-based tag emotion aware recommendation with probabilistic ranking", author="Hyewon Lim and Hyoung-Joo Kim and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2019.12.003}, volume={13}, number={12}, year="2019", month={December}, pages={5826-5841}}