TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Tensor-based tag emotion aware recommendation with probabilistic ranking |
| 영문제목(English Title) |
Tensor-based tag emotion aware recommendation with probabilistic ranking |
| 저자(Author) |
Hyewon Lim
Hyoung-Joo Kim
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| 원문수록처(Citation) |
VOL 13 NO. 12 PP. 5826 ~ 5841 (2019. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
Recommendation
tag
High-Order Singular Value Decomposition (HOSVD)
BM25
item-based filtering
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| 파일첨부 |
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