| | |
| | |
Stat |
Members: 3645 Articles: 2'504'928 Articles rated: 2609
26 April 2024 |
|
| | | |
|
Article overview
| |
|
Leveraging Historical Interaction Data for Improving Conversational Recommender System | Kun Zhou
; Wayne Xin Zhao
; Hui Wang
; Sirui Wang
; Fuzheng Zhang
; Zhongyuan Wang
; Ji-Rong Wen
; | Date: |
19 Aug 2020 | Abstract: | Recently, conversational recommender system (CRS) has become an emerging and
practical research topic. Most of the existing CRS methods focus on learning
effective preference representations for users from conversation data alone.
While, we take a new perspective to leverage historical interaction data for
improving CRS. For this purpose, we propose a novel pre-training approach to
integrating both item-based preference sequence (from historical interaction
data) and attribute-based preference sequence (from conversation data) via
pre-training methods. We carefully design two pre-training tasks to enhance
information fusion between item- and attribute-based preference. To improve the
learning performance, we further develop an effective negative sample generator
which can produce high-quality negative samples. Experiment results on two
real-world datasets have demonstrated the effectiveness of our approach for
improving CRS. | Source: | arXiv, 2008.08247 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |