LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Taeho | - |
dc.contributor.author | Yu, Juwon | - |
dc.contributor.author | Shin, Won-Yong | - |
dc.contributor.author | Lee, Hyunyoung | - |
dc.contributor.author | Im, Ji-Hui | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2023-09-26T09:41:34Z | - |
dc.date.available | 2023-09-26T09:41:34Z | - |
dc.date.created | 2023-09-25 | - |
dc.date.issued | 2023-08 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/191243 | - |
dc.description.abstract | For high-quality conversational recommender systems (CRS), it is important to recommend the suitable items by capturing the items' features mentioned in the dialog and to explain the appropriate ones among the various features of the recommended item. We argue that the CRS model should be a domain-expert who is (1) knowledgeable about the relationships between items and their various features and (2) able to explain the recommended item with its features relevant to dialog context. To this end, we propose a novel framework, named as LATTE, to pre-train each core module in CRS (i.e., the recommendation and the conversation module) through abundant external data. For the recommendation module, we pre-train the recommendation module to comprehensively understand the relationships between items and their various features by leveraging both multi-reviews and a knowledge graph. For pre-training the conversation module, we create the synthetic dialogs, which contain responses providing the explanation relevant to the dialog context by using all the items' features and dialog templates. Through extensive experiments on two public CRS datasets, we demonstrate that LATTE exhibits (1) the effectiveness of each module in LATTE, (2) the superiority over 7 state-of-the art methods, and (3) the interpretations based on visualization. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3580305.3599401 | - |
dc.identifier.scopusid | 2-s2.0-85171363641 | - |
dc.identifier.bibliographicCitation | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1144 - 1153 | - |
dc.relation.isPartOf | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | - |
dc.citation.title | Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | - |
dc.citation.startPage | 1144 | - |
dc.citation.endPage | 1153 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Knowledge graph | - |
dc.subject.keywordPlus | Knowledge management | - |
dc.subject.keywordPlus | Recommender systems | - |
dc.subject.keywordPlus | Conversational recommendations | - |
dc.subject.keywordPlus | Conversational recommender systems | - |
dc.subject.keywordPlus | Domain experts | - |
dc.subject.keywordPlus | Explanation. | - |
dc.subject.keywordPlus | High quality | - |
dc.subject.keywordPlus | Knowledge graphs | - |
dc.subject.keywordPlus | Pre-training | - |
dc.subject.keywordPlus | State-of-the-art methods | - |
dc.subject.keywordPlus | System models | - |
dc.subject.keywordAuthor | conversational recommender systems | - |
dc.subject.keywordAuthor | domain-expert | - |
dc.subject.keywordAuthor | explanation. | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3580305.3599401 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.