Leveraging Order-Free Tag Relations for Context-Aware Recommendation

Junmo Kang, Jeonghwan Kim, Suwon Shin, Sung-Hyon Myaeng


Abstract
Tag recommendation relies on either a ranking function for top-k tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicting yet desirable characteristics of a tag set: orderlessness and inter-dependency. While the ranking approach fails to address the inter-dependency among tags when they are ranked, the autoregressive approach fails to take orderlessness into account because it is designed to utilize sequential relations among tokens. We propose a sequence-oblivious generation method for tag recommendation, in which the next tag to be generated is independent of the order of the generated tags and the order of the ground truth tags occurring in training data. Empirical results on two different domains, Instagram and Stack Overflow, show that our method is significantly superior to the previous approaches.
Anthology ID:
2021.emnlp-main.279
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3464–3476
Language:
URL:
https://aclanthology.org/2021.emnlp-main.279
DOI:
10.18653/v1/2021.emnlp-main.279
Bibkey:
Cite (ACL):
Junmo Kang, Jeonghwan Kim, Suwon Shin, and Sung-Hyon Myaeng. 2021. Leveraging Order-Free Tag Relations for Context-Aware Recommendation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3464–3476, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Leveraging Order-Free Tag Relations for Context-Aware Recommendation (Kang et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.279.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.279.mp4