---
title: "Towards Conversational Recommendation over Multi-Type Dialogs"
description: "This paper defines a new task: a bot that, without revealing its plan, gradually moves a conversation… 43 references, cited by 62 papers here."
authors: ["Zeming Liu", "Haifeng Wang", "Zheng-Yu Niu", "Hua Wu", "Wanxiang Che", "Ting Liu"]
year: 2020
venue: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/2020.acl-main.98/"
doi: "10.18653/v1/2020.acl-main.98"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2020-conversational-recommendation-over-multi-type/"
---
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# Towards Conversational Recommendation over Multi-Type Dialogs

**Authors:** [Zeming Liu](https://md.research.elamur.ai/free/en/authors/zeming-liu/), [Haifeng Wang](https://md.research.elamur.ai/free/en/authors/haifeng-wang/), [Zheng-Yu Niu](https://md.research.elamur.ai/free/en/authors/zheng-yu-niu/), [Hua Wu](https://md.research.elamur.ai/free/en/authors/hua-wu/), [Wanxiang Che](https://md.research.elamur.ai/free/en/authors/wanxiang-che/) and [Ting Liu](https://md.research.elamur.ai/free/en/authors/ting-liu/) · **Year:** 2020 · **Venue:** [Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2020](https://md.research.elamur.ai/free/en/venues/acl/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.acl-main.98/) · [PDF](https://aclanthology.org/2020.acl-main.98.pdf) · [DOI 10.18653/v1/2020.acl-main.98](https://doi.org/10.18653/v1/2020.acl-main.98) · [OpenAlex](https://openalex.org/W3035355914)

**Citations:** 168 citations (OpenAlex, 2026-10-14) · 62 in our library, of the 141,840 papers whose bibliographies we read

## Summary
Earlier conversational recommender research mostly handles one kind of dialog and assumes the user knows the conversation's purpose from the start. This paper defines a new task: a bot that, without revealing its plan, gradually moves a conversation from question answering, chitchat or a task dialog toward recommending an item, while adapting to the user's interests and reactions. To support this task, the authors built DuRecDial at Baidu. It is a human-to-human Chinese corpus grounded in a knowledge graph, with explicit seeker profiles and several consecutive sessions for each seeker–recommender pair. They also propose MGCG, a multi-goal driven generation framework with two parts. A goal-planning module selects a chain of goals, each a dialog type plus a topic, that ends in a recommendation target. A goal-guided module then produces the responses. Both a retrieval variant and a generation variant clearly beat a seq2seq baseline in automatic and human evaluation, and supplying goals and knowledge improves results. The dataset gives a demanding benchmark for proactive, personalized, mixed-type dialog systems.

## Key points
- **DuRecDial** has about 10.2k dialogs and 156k utterances. It covers recommendation, chitchat, QA and task dialogs across domains such as movies, music, movie stars, food, restaurants, news and weather, and includes user profiles. Data and code are on GitHub (PaddlePaddle).
- **MGCG** comes in two variants, MGCG_R (retrieval-based) and MGCG_G (generation-based), and is compared against S2S. MGCG_R scores better on Hits@k and DIST-2, while MGCG_G is stronger on knowledge F1.
- **Topic prediction** is difficult because there are roughly 1000 candidate topics.
- **Human evaluation** collected 100 dialogs per model. Overall completed goals rose from 109 (S2S) to 158 (MGCG_R) and 174 (MGCG_G), and knowledge usage rose from 14 to 46 and 57.
- **Goal completion by dialog type:** chitchat goals are the easiest to complete, while QA and recommendation goals are harder. Knowledge selection appears closely tied to successful goal completion.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 76 works · 27,918 citations
- [Baidu](https://md.research.elamur.ai/free/en/companies/baidu/) — Company, Beijing, China · 40 works · 14,627 citations

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## Cite

Cite as: Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che and Ting Liu. 2020. Towards Conversational Recommendation over Multi-Type Dialogs. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2020conversational,
  title = {{Towards Conversational Recommendation over Multi-Type Dialogs}},
  author = {Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che and Ting Liu},
  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.acl-main.98},
  url = {https://doi.org/10.18653/v1/2020.acl-main.98}
}
```

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## References

43 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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17. Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen tau Yih, and Michel Galley. 2018. A knowledge-grounded neural conversation model. In AAAI. [find](https://scholar.google.com/scholar?q=Marjan+Ghazvininejad%2C+Chris+Brockett%2C+Ming%2DWei+Chang%2C+Bill+Dolan%2C+Jianfeng+Gao%2C+Wen+tau+Yih%2C+and+Michel+Galley%2E+2018%2E+A+knowledge%2Dgrounded+neural+conversation+model%2E+In+AAAI%2E)
18. Jianheng Tang, Tiancheng Zhao, Chengyan Xiong, Xiaodan Liang, Eric P Xing, and Zhiting Hu. 2019a. [Target-guided open-domain conversation](https://md.research.elamur.ai/free/en/papers/tang-2019-target-guided-open-domain-conversation/). In ACL.
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26. Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, and Haifeng Wang. 2019. [Proactive human-machine conversation with explicit conversation goal](https://md.research.elamur.ai/free/en/papers/wu-2019-proactive-human-machine-conversation-explicit/). In ACL.
27. Gender: We randomely select ”male” or ”female” as the seeker’s gender. [find](https://scholar.google.com/scholar?q=Gender%3A+We+randomely+select+%E2%80%9Dmale%E2%80%9D+or+%E2%80%9Dfemale%E2%80%9D+as+the+seeker%E2%80%99s+gender%2E)
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29. Age range: We randomly choose one from the 5 age ranges. [find](https://scholar.google.com/scholar?q=Age+range%3A+We+randomly+choose+one+from+the+5+age+ranges%2E)
30. Lili Yao, Yaoyuan Zhang, Yansong Feng, Dongyan Zhao, and Rui Yan. 2017. Towards implicit content-introducing for generative short-text conversation systems. In EMNLP. [find](https://scholar.google.com/scholar?q=Lili+Yao%2C+Yaoyuan+Zhang%2C+Yansong+Feng%2C+Dongyan+Zhao%2C+and+Rui+Yan%2E+2017%2E+Towards+implicit+content%2Dintroducing+for+generative+short%2Dtext+conversation+systems%2E+In+EMNLP%2E)
31. Residential city: We randomly choose one from 55 cities in China as the seeker’s residential city. [find](https://scholar.google.com/scholar?q=Residential+city%3A+We+randomly+choose+one+from+55+cities+in+China+as+the+seeker%E2%80%99s+residential+city%2E)
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33. Occupation status: We randomly choose one from ”student”, ”worker” and ”retirement” based on above age range. [find](https://scholar.google.com/scholar?q=Occupation+status%3A+We+randomly+choose+one+from+%E2%80%9Dstudent%E2%80%9D%2C+%E2%80%9Dworker%E2%80%9D+and+%E2%80%9Dretirement%E2%80%9D+based+on+above+age+range%2E)
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35. Domain preference: We randomly select one or two domains as ones that the seeker likes (e.g., movie, food), and one domain as the one that the seeker dislikes (e.g., news). It will affect the setting of task templates for this seeker. [find](https://scholar.google.com/scholar?q=Domain+preference%3A+We+randomly+select+one+or+two+domains+as+ones+that+the+seeker+likes+%28e%2Eg%2E%2C+movie%2C+food%29%2C+and+one+domain+as+the+one+that+the+seeker+dislikes+%28e%2Eg%2E%2C+news%29%2E+It+will+affect+the+setting+)
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37. Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017. [Learning discourse-level diversity for neural dialog models using conditional variational autoencoders](https://md.research.elamur.ai/free/en/papers/zhao-2017-learning-discourse-level-diversity-neural/). In ACL.
38. Seed entity preference: We randomly select one or two entities from KG entities of the domains preferred by the seeker as his/her preference at entity level. It will affect the setting of task templates for this seeker. [find](https://scholar.google.com/scholar?q=Seed+entity+preference%3A+We+randomly+select+one+or+two+entities+from+KG+entities+of+the+domains+preferred+by+the+seeker+as+his%2Fher+preference+at+entity+level%2E+It+will+affect+the+setting+of+task+templat)
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40. Rejected entity list and accepted entity list: Both of them are empty at the beginning, and they will be updated as the conversation progresses; the two lists will affect the recommendation results of subsequent conversations to some extent. [find](https://scholar.google.com/scholar?q=Rejected+entity+list+and+accepted+entity+list%3A+Both+of+them+are+empty+at+the+beginning%2C+and+they+will+be+updated+as+the+conversation+progresses%3B+the+two+lists+will+affect+the+recommendation+results+of)
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42. Li Zhou, Jianfeng Gao, Di Li, and Heung-Yeung Shum. 2018b. The design and implementation of xiaoice, an empathetic social chatbot. In CoRR, abs/1812.08989. [find](https://scholar.google.com/scholar?q=Li+Zhou%2C+Jianfeng+Gao%2C+Di+Li%2C+and+Heung%2DYeung+Shum%2E+2018b%2E+The+design+and+implementation+of+xiaoice%2C+an+empathetic+social+chatbot%2E+In+CoRR%2C+abs%2F1812%2E08989%2E)
43. Collection of Knowledge graph (KG) The domain of knowledge graph include stars, movies, music, news, food, POI(Point of Interest), weather. [find](https://scholar.google.com/scholar?q=Collection+of+Knowledge+graph+%28KG%29+The+domain+of+knowledge+graph+include+stars%2C+movies%2C+music%2C+news%2C+food%2C+POI%28Point+of+Interest%29%2C+weather%2E)

*Updated 24 October 2026*