A Conversational Recommender System (CRS) is defined by Gao et al. (2021) as following:
A recommendation system that can elicit the dynamic preferences of users and take actions based on their current needs through real-time multi-turn interactions using natural language.
A quick-start paper list including survey, tutorial, toolkit and model papers.
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"Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems".
arXiv(2020)
[PDF] -
"Tutorial on Conversational Recommendation Systems".
RecSys(2020)
[PDF] [Homepage] -
CRSLab: "CRSLab: An Open-Source Toolkit for Building Conversational Recommender System".
ACL(2021)
[PDF] [Homepage] -
CRM: "Conversational Recommender System".
SIGIR(2018)
[PDF] [Homepage] -
SAUR: "Towards Conversational Search and Recommendation: System Ask, User Respond".
CIKM(2018)
[PDF] [Dataset] -
EAR: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems".
WSDM(2020)
[PDF] [Homepage] -
CPR: "Interactive Path Reasoning on Graph for Conversational Recommendation".
KDD(2020)
[PDF] [Homepage] -
ReDial: "Towards Deep Conversational Recommendations".
NeurIPS(2018)
[PDF] [Dataset] [Code] -
KBRD: "Towards Knowledge-Based Recommender Dialog System".
EMNLP-IJCNLP(2019)
[PDF] [Code] -
KGSF: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion".
KDD(2020)
[PDF] [Code]
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"Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems".
arXiv(2020)
[PDF] -
"A survey on conversational recommender systems".
arXiv(2020)
[PDF] -
"Advances and Challenges in Conversational Recommender Systems: A Survey".
arXiv(2021)
[PDF]
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"Tutorial on Conversational Recommendation Systems". [Homepage]
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"Conversational Recommendation: Formulation, Methods, and Evaluation".
SIGIR(2020)
[PDF] [Slides]
- CRSLab: "CRSLab: An Open-Source Toolkit for Building Conversational Recommender System".
ACL(2021)
[PDF] [Homepage]
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ConvRec: "Conversational Recommender System".
SIGIR(2018)
[PDF] [Homepage] -
SAUR: "Towards Conversational Search and Recommendation: System Ask, User Respond".
CIKM(2018)
[PDF] [Download] -
EAR: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems".
WSDM(2020)
[PDF] [Homepage] -
CPR: "Interactive Path Reasoning on Graph for Conversational Recommendation".
KDD(2020)
[PDF] [Homepage] -
ReDial: "Towards Deep Conversational Recommendations".
NeurIPS(2018)
[PDF] [Homepage] -
OpenDialKG: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs".
ACL(2019)
[PDF] [Homepage] -
PersuasionForGood: "Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good".
ACL(2019)
[PDF] [Homepage] -
CCPE: "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences".
SIGDial(2019)
[PDF] [Homepage] -
TG-ReDial: "Towards Topic-Guided Conversational Recommender System".
COLING(2020)
[PDF] [Homepage] -
GoRecDial: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue".
EMNLP(2019)
[PDF] [Download] -
DuRecDial: "Towards Conversational Recommendation over Multi-Type Dialogs".
ACL(2020)
[PDF] [Download] -
INSPIRED: "INSPIRED: Toward Sociable Recommendation Dialogue Systems".
EMNLP(2020)
[PDF] [Homepage] -
MGConvRex: "User Memory Reasoning for Conversational Recommendation".
ACL(2020)
[PDF] -
COOKIE: "COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce".
arXiv(2020)
[PDF] [Homepage] -
IARD: "Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations".
UMAP(2020)
[PDF] [Homepage]
Attribute-based CRSs typically capture user preferences by asking queries about item attributes and generates responses using pre-defined templates.
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"Towards Conversational Recommender Systems".
KDD(2016)
[PDF] -
CRM: "Conversational Recommender System".
SIGIR(2018)
[PDF] [Homepage] -
SAUR: "Towards Conversational Search and Recommendation: System Ask, User Respond".
CIKM(2018)
[PDF] [Dataset] -
Q&R: "Q&R: A Two-Stage Approach toward Interactive Recommendation".
KDD(2018)
[PDF] -
"Dialogue based recommender system that flexibly mixes utterances and recommendations".
WI(2019)
[Link] -
EAR: "Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems".
WSDM(2020)
[PDF] [Homepage] -
CPR: "Interactive Path Reasoning on Graph for Conversational Recommendation".
KDD(2020)
[PDF] [Homepage] -
CRSAL: "CRSAL: Conversational Recommender Systems with Adversarial Learning".
TOIS(2020)
[PDF] [Code] -
Qrec: "Towards Question-Based Recommender Systems".
SIGIR(2020)
[PDF] [Code] -
ConTS: "Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users".
TOIS(2021)
[PDF] [Code] -
UNICORN: "Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning".
SIGIR(2021)
[PDF] -
KBQG: "Learning to Ask Appropriate Questions in Conversational Recommendation".
arXiv(2021)
[PDF] [Code] -
"Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation".
WSDM(2021)
[Link] -
"Developing a Conversational Recommendation System for Navigating Limited Options".
CHI(2021)
[PDF]
Compared to attribute-based CRSs, generation-based CRSs pay more attention to generate human-like responses in natural language.
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ReDial: "Towards Deep Conversational Recommendations".
NeurIPS(2018)
[PDF] [Code] [Dataset] -
KBRD: "Towards Knowledge-Based Recommender Dialog System".
EMNLP-IJCNLP(2019)
[PDF] [Code] -
GoRecDial: "Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue".
EMNLP(2019)
[PDF] [Code] [Dataset] -
DialKG Walker: "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs".
ACL(2019)
[PDF] [Code] [Dataset] -
DCR: "Deep Conversational Recommender in Travel".
TKDE(2020)
[PDF] [Code] -
KGSF: "Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion".
KDD(2020)
[PDF] [Code] -
MGCG: "Towards Conversational Recommendation over Multi-Type Dialogs".
ACL(2020)
[PDF] [Code] [Dataset] -
ECR: "Towards Explainable Conversational Recommendation".
IJCAI(2020)
[PDF] -
INSPIRED: "INSPIRED: Toward Sociable Recommendation Dialogue Systems".
EMNLP(2020)
[PDF] [Homepage] -
TG-ReDial: "Towards Topic-Guided Conversational Recommender System".
COLING(2020)
[PDF] [Homepage] -
MGConvRex: "User Memory Reasoning for Conversational Recommendation".
COLING(2020)
[PDF] -
KGConvRec: "Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation".
COLING(2020)
[PDF] [Code] -
CR-Walker: "Bridging the Gap between Conversational Reasoning and Interactive Recommendation".
arXiv(2020)
[PDF] [Code] -
RevCore: "RevCore: Review-augmented Conversational Recommendation".
arXiv(2021)
[PDF] [Code] -
KECRS: "KECRS: Towards Knowledge-Enriched Conversational Recommendation System".
arXiv(2021)
[PDF] -
"Category Aware Explainable Conversational Recommendation".
arXiv(2021)
[PDF]
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Converse-Et-Impera: "Converse-Et-Impera: Exploiting Deep Learning and Hierarchical Reinforcement Learning for Conversational Recommender Systems".
AI*IA(2017)
[PDF] -
"A Model of Social Explanations for a Conversational Movie Recommendation System".
HAI(2019)
[PDF] -
"Dynamic Online Conversation Recommendation".
ACL(2020)
[PDF] [Code] -
IAI MovieBot: "IAI MovieBot: A Conversational Movie Recommender System".
CIKM(2020)
[PDF] [Code] -
ConUCB: "Conversational Contextual Bandit: Algorithm and Application".
WWW(2020)
[PDF] [Code] -
Cora: "A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations".
HAI(2020)
[PDF] -
"Conversational Music Recommendation based on Bandits".
ICKG(2020)
[Link] -
n-by-p: "Navigation-by-preference: a new conversational recommender with preference-based feedback".
IUI(2020)
[PDF] -
"A Bayesian Approach to Conversational Recommendation Systems".
AAAI Workshop(2020)
[PDF]
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CCPE: "Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences".
SIGDial(2019)
[PDF] [Dataset] -
"Leveraging Historical Interaction Data for Improving Conversational Recommender System".
CIKM(2020)
[PDF] [Code] -
"Evaluating Conversational Recommender Systems via User Simulation".
KDD(2020)
[PDF] [Code] -
"End-to-End Learning for Conversational Recommendation: A Long Way to Go?".
RecSys(2020)
[PDF] [Material] -
"What Does BERT Know about Books, Movies and Music? Probing BERT for Conversational Recommendation".
RecSys(2020)
[PDF] [Code] -
"Latent Linear Critiquing for Conversational Recommender Systems".
WWW(2020)
[PDF] [Code] -
"A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems".
RecSys(2020)
[Link] [Code] -
"A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation".
LREC(2020)
[PDF] -
"Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations".
UMAP(2020)
[PDF] [Dataset] -
ConveRSE: "Conversational Recommender Systems and natural language: A study through the ConveRSE framework".
Decision Support Systems(2020)
[Link] [Dataset] -
"On Estimating the Training Cost of Conversational Recommendation Systems".
arXiv(2020)
[PDF]
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"Recommendation in Dialogue Systems". By Yueming Sun(2019). [PDF]
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"Advanced Method Towards Conversational Recommendation". By Yisong Miao(2020). [PDF]