CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling

Jinfeng Zhou, Bo Wang, Zhitong Yang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou


Abstract
Conversational recommendation systems (CRS) aim to determine a goal item by sequentially tracking users’ interests through multi-turn conversation. In CRS, implicit patterns of user interest sequence guide the smooth transition of dialog utterances to the goal item. However, with the convenient explicit knowledge of knowledge graph (KG), existing KG-based CRS methods over-rely on the explicit separate KG links to model the user interests but ignore the rich goal-aware implicit interest sequence patterns in a dialog. In addition, interest sequence is also not fully used to generate smooth transited utterances. We propose CR-GIS with a parallel star framework. First, an interest-level star graph is designed to model the goal-aware implicit user interest sequence. Second, a hierarchical Star Transformer is designed to guide the multi-turn utterances generation with the interest-level star graph. Extensive experiments verify the effectiveness of CR-GIS in achieving more accurate recommended items with more fluent and coherent dialog utterances.
Anthology ID:
2022.coling-1.32
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
400–411
Language:
URL:
https://aclanthology.org/2022.coling-1.32
DOI:
Bibkey:
Cite (ACL):
Jinfeng Zhou, Bo Wang, Zhitong Yang, Dongming Zhao, Kun Huang, Ruifang He, and Yuexian Hou. 2022. CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling. In Proceedings of the 29th International Conference on Computational Linguistics, pages 400–411, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling (Zhou et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.32.pdf
Data
InspiredOpenDialKGTG-ReDial