Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Refining Conversational Case Libraries
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A robust and efficient three-layered dialogue component for a speech-to-speech translation system
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Reinforcement Learning Architecture for Web Recommendations
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Feature Selection Methods for Conversational Recommender Systems
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
IEEE Transactions on Knowledge and Data Engineering
An MDP-Based Recommender System
The Journal of Machine Learning Research
Personalization technologies: a process-oriented perspective
Communications of the ACM - The digital society
An Adaptive Bilateral Negotiation Model for E-Commerce Settings
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
A decision-theoretic approach to task assistance for persons with dementia
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Knowledge-Based Systems
Product recommendation with interactive query management and twofold similarity
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Adapting the interaction state model in conversational recommender systems
Proceedings of the 10th international conference on Electronic commerce
Improving recommender systems with adaptive conversational strategies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Designing a Metamodel-Based Recommender System
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Who is talking about what: social map-based recommendation for content-centric social websites
Proceedings of the fourth ACM conference on Recommender systems
Who is Doing What and When: Social Map-Based Recommendation for Content-Centric Social Web Sites
ACM Transactions on Intelligent Systems and Technology (TIST)
Understanding buyers' social information needs during purchase decision process
Proceedings of the 12th International Conference on Electronic Commerce: Roadmap for the Future of Electronic Business
Semi-automatic generation of recommendation processes and their GUIs
Proceedings of the 2013 international conference on Intelligent user interfaces
Tutorial on application-oriented evaluation of recommendation systems
AI Communications
| Hi-index | 0.00 |
Recommender systems are intelligent E-commerce applications that assist users in a decision-making process by offering personalized product recommendations during an interaction session. Quite recently, conversational approaches have been introduced in order to support more interactive recommendation sessions. Notwithstanding the increased interactivity offered by these approaches, the system employs an interaction strategy that is specified apriori (at design time) and followed quite rigidly during the interaction. In this paper, we present a new type of recommender system which is capable of learning autonomously an adaptive interaction strategy for assisting the users in acquiring their interaction goals. We view the recommendation process as a sequential decision problem and we model it as a Markov Decision Process (MDP). We learn a model of the user behavior, and use it to acquire the adaptive strategy using Reinforcement Learning (RL) techniques. In this context, the system learns the optimal strategy by observing the consequences of its actions on the users and also on the final outcome of the recommendation session. We apply our approach within an existing travel recommender system which uses a rigid, non-adaptive support strategy for advising a user in refining a query to a travel product catalogue. The initial results demonstrate the value of our approach and show that our system is able to improve the non-adaptive strategy in order to learn an optimal (adaptive) recommendation strategy.