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.
- Changchien, S. W., Chin-Feng, L. and Yu-Jung, H. On-line personalized sales promotion in electronic commerce. Expert Systems with Applications, 2004, 35--52. Google ScholarDigital Library
- Joachims, T., Freitag, D. and Mitchell, T. M. WebWatcher: A tour guide for the World Wide Web, Proceedings of International Joint Conference on Artificial Intelligence, 1997. 770--775.Google Scholar
- Lieberman, H. L. An agent that assist web browsing, Proceedings of International Joint Conference on Artificial Intelligence, 1995, 475--480. Google ScholarDigital Library
- Mitchell, T. M. Machine Learning, McGraw-Hill Companies, Inc., New York, 1997. Google ScholarDigital Library
- Seo, Y. W. and Zhang, B. T. Personalized Web-Document Filtering Using Reinforcement Learning, Applied Artificial Intelligence, 2001, 665--685.Google Scholar
- Srikumar, K., and Bhasker B. Personalized Product Selection in Internet Business. Journal of Electronic Commerce Research. (5), 2004, 216--227.Google Scholar
- Sukonmanee, P. and Srivihok, A. 2004. Personalisation Travel Support Engine Using Reinforcement Learning. Proceeding of International Conference on Knowledge Management in Asia Pacific KMAP 2004, Taipei, Taiwan, China, 6--9 December 2004.Google Scholar
- Sutton, R. S. and Barto, A. G. Reinforcement Learning: An In troduction, MIT Press, Cambridge, 1998. Google ScholarDigital Library
- Yuan, S. T. A personalized and integrative comparison-shopping system and its applications, Decision Support Systems, 2003, 139--156. Google ScholarDigital Library
Index Terms
- E-commerce intelligent agent: personalization travel support agent using Q Learning
Recommendations
Understanding collaborative filtering parameters for personalized recommendations in e-commerce
Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized ...
Item-Based Filtering and Semantic Networks for Personalized Web Content Adaptation in E-Commerce
SETN '08: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and ApplicationsPersonalised web content adaptation systems are critical constituents of successful e-commerce applications. These systems aim at the automatic identification, composition and presentation of content to users based on a model about their preferences ...
Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts
Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the ...
Comments