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E-commerce intelligent agent: personalization travel support agent using Q Learning

Published: 15 August 2005 Publication History

Abstract

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.

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  1. E-commerce intelligent agent: personalization travel support agent using Q Learning

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    cover image ACM Other conferences
    ICEC '05: Proceedings of the 7th international conference on Electronic commerce
    August 2005
    957 pages
    ISBN:1595931120
    DOI:10.1145/1089551
    • Conference Chairs:
    • Qi Li,
    • Ting-Peng Liang
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 August 2005

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    Author Tags

    1. Q Learning
    2. e-commerce
    3. intelligent agent
    4. personalisation

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    • (2025)Examining Policy Entropy of Reinforcement Learning Agents for Personalization TasksPattern Recognition and Artificial Intelligence10.1007/978-981-97-8702-9_33(493-504)Online publication date: 8-Feb-2025
    • (2024)A reinforcement learning recommender system using bi-clustering and Markov Decision ProcessExpert Systems with Applications10.1016/j.eswa.2023.121541237(121541)Online publication date: Mar-2024
    • (2023)A Survey on Reinforcement Learning and Deep Reinforcement Learning for Recommender SystemsDeep Learning Theory and Applications10.1007/978-3-031-39059-3_26(385-402)Online publication date: 31-Jul-2023
    • (2022)Interactive Reinforcement Learning-Based API Recommendation2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC)10.1109/CTISC54888.2022.9849805(1-5)Online publication date: 22-Apr-2022
    • (2022)Proximal policy optimization based hybrid recommender systems for large scale recommendationsMultimedia Tools and Applications10.1007/s11042-022-14231-x82:13(20079-20100)Online publication date: 15-Dec-2022
    • (2021)Deep Reinforcement Learning based Recommender System with State Representation2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671687(5703-5707)Online publication date: 15-Dec-2021
    • (2020)Reinforcement learning for personalization: A systematic literature reviewData Science10.3233/DS-2000283:2(107-147)Online publication date: 10-Apr-2020
    • (2020)A Model-Free Approach to Meta-Level Control of Anytime Algorithms2020 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA40945.2020.9196898(11436-11442)Online publication date: May-2020
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    • (2018)Explore, exploit, and explainProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3240354(31-39)Online publication date: 27-Sep-2018
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