Towards Conversational Recommender Systems
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Summary
This paper develops a preference elicitation framework to identify which questions to ask a new user to quickly learn their preferences, and finds that this framework can make very effective use of online user feedback, improving personalized recommendations over a static model by 25% after asking only 2 questions.
- Type
- article
- Published
- 2016-08-13
- Cited by
- 479
- References
- 54
- OpenAlex
- https://openalex.org/W2349436533
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11744847
Keywords
Recommender system, Computer science, Ask price, Exploit, Preference
References
- Individualized rank aggregation using nuclear norm regularization
- Gambling in a rigged casino: The adversarial multi-armed bandit problem
- Qualitative research practice
- Elements of a Plan-Based Theory of Speech Acts
- Expectation Propagation for approximate Bayesian inference
- Critiquing-based recommenders: survey and emerging trends
- Choice-based preference elicitation for collaborative filtering recommender systems
- CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering
- Personalized Recommendation via Parameter-Free Contextual Bandits
- Ensemble contextual bandits for personalized recommendation
- Item-based collaborative filtering recommendation algorithms
- Active Learning for Recommender Systems
- Collaborative learning of preference rankings
- Eliciting the users' unknown preferences
- Matrix Factorization Techniques for Recommender Systems
- Regression-based latent factor models
- Improving the User Experience during Cold Start through Choice-Based Preference Elicitation
- Relative confidence sampling for efficient on-line ranker evaluation
- Interactive collaborative filtering
- Learning multiple-question decision trees for cold-start recommendation
Cited by
- A Theoretical Framework for Conversational Search
- Chatbots As A Mean To Motivate Behavior Change : How To Inspire Pro-Environmental Attitude with Chatbot Interfaces
- Chatbots as conversational recommender systems in urban contexts
- Cold-Start Developer Recommendation in Software Crowdsourcing: A Topic Sampling Approach
- Deep Learning for Recommender Systems
- Defining and Supporting Narrative-driven Recommendation
- A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning
- Multi-Criteria Recommender Systems: A Survey and a Method to Learn New User's Profile
- A survey on solving cold start problem in recommender systems
- Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
- Towards an Optimal Dialog Strategy for Information Retrieval Using Both Open- and Close-ended Questions
- Interactive Storytelling for Movie Recommendation through Latent Semantic Analysis
- Vote Goat: Conversational Movie Recommendation
- An Argumentation-based Conversational Recommender System for Recommending Learning Objects
- Q&R: A Two-Stage Approach toward Interactive Recommendation
- Conceptualizing agent-human interactions during the conversational search process
- Investigating how conversational search agents affect user's behaviour, performance and search experience
- Research Frontiers in Information Retrieval: Report from the Third Strategic Workshop on Information Retrieval in Lorne (SWIRL 2018)
- A state-of-the-art Recommender Systems: An overview on Concepts, Methodology and Challenges
- Preference elicitation as an optimization problem
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