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Learning UserAdapted Strategies in Conversational Recommender Systems,Used
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This book introduces, describes and validates a novel technology for Conversational Recommender Systems (CRSs). It is targeted for researchers, teachers and students related to the fields of Machine Learning and/or Ecommerce. Specifically, CRSs are intelligent Ecommerce applications that assist users by supporting an interactive recommendation process. To this end, CRSs employ some type of a recommendation strategy, i.e., a specification of the system behavior. Typically, this strategy is predetermined in advance and hardcoded inside the system, thus making it possibly nonadapted to the dynamic needs of the users. The technology presented in this book allows CRSs to autonomously learn the optimal (best) strategy for a given recommendation context, from amongst a set of available ones. The optimal strategy is best adapted to the users' needs, and is learned using Reinforcement Learning techniques (a branch of Machine Learning). We have validated this technology through simulations as well as in an online evaluation involving several hundreds of real users. Our results justify the application of this technology in stateoftheart Ecommerce portals.
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.