Carte Learning User-Adapted Strategies in Conversational Recommender Systems Tariq Mahmood

Learning User-Adapted Strategies in Conversational Recommender Systems

Application of Reinforcement Learning To E-commerce Portals For Learning a System Behavior That Is Adapted To The Users In An Interaction Context

Autor: Tariq Mahmood
Limbă: engleză
Legare: Carte broșată
Editura: VDM Verlag
Disponibilitate: La editor doar la comandă
Expediem în 17-27 zile
405.77 lei
This book introduces, describes and validates a novel technology for Conversational Recommender Syst...

Informații despre carte

Limbă
engleză
Legare
Carte - Carte broșată
Publicat
2010
Pagini
224
EAN
9783639079791
ISBN
3639079795
Enbook ID
06817204
Editura
Greutate
336
Dimensiuni
152 x 229 x 13

Descriere completă

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 E-commerce. Specifically, CRSs are intelligent E-commerce 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 pre-determined in advance and hard-coded inside the system, thus making it possibly non-adapted 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 state-of-the-art E-commerce portals.

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