Proceedings ArticleDOI
E-commerce intelligent agent: personalization travel support agent using Q Learning
Anongnart Srivihok,Pisit Sukonmanee +1 more
- pp 287-292
TLDR
The results from this study reveal that it is possible to develop Personalised Support System and using weighted trip features improve effectiveness and increase the accuracy of the personalized engine.Abstract:Â
Recently information technology (IT) plays a significant role in business environment, enterprises use IT in the competitive world market. Web personalization and one to one marketing have been introduced as strategy and marketing tools. By using historical and present information of customers, organizations can learn, predict customer's behaviors and develop products or services best suited to potential customers.In this study, a Personalized Support System is suggested to manage traveling information for user. It provides the information that matches the users' interests. This system applies the Q Learning algorithm to analyze, learn customer behaviors and then it recommend products to meet customer interests. There are two learning approaches using in this study. First, Personalization Learner by Cluster Properties is learning from all users in one cluster to find the cluster interests of travel information by using given data on user ages and genders. Second, Personalization Learner by User Behavior: user profile, user behaviors and trip features will be analyzed to find the unique interest of each web user. The results from this study reveal that it is possible to develop Personalised Support System. Using weighted trip features improve effectiveness and increase the accuracy of the personalized engine. Precision, Recall and Harmonic Mean of the learned system are higher than the original one. This study offers new and fruitful information in the areas of web personalisation in tourist industry as well as in e-Commerce.read more
Citations
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Explore, exploit, and explain: personalizing explainable recommendations with bandits
James McInerney,Benjamin Lacker,Samantha Hansen,Karl Higley,Hugues Bouchard,Alois Gruson,Rishabh Mehrotra +6 more
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Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
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Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
TL;DR: In this article, a multi-armed bandit is proposed to learn user preferences, which accounts for both audio content and the novelty of recommendations, and a piecewise linear approximation to the model and a variational inference algorithm are employed to speed up Bayesian inference.
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