Solution Architecture for Visitor Segmentation and Recommendation Generation in Real Time
With increasing product portfolios of eCommerce companies it is getting harder for their customers to find the products they like best. A solution to this problem is to analyze the customer’s behavior, and recommend products based on ratings. By considering click-stream data, the customer is unburdened with explicitly rating his favored. In this paper, we introduce a system for segmenting visitors and recommending adequate items in real time by using an event-based system called Sense and Respond Infrastructure (SARI) for processing click-stream data. We present the architecture and components for a real-time click-stream analysis which can be easily customized to business needs of domain experts and business users. SARI provides functionality to monitor visitor and customer behavior, respond accordingly and at the same time optimize and adapt customer processes in real time. To illustrate this approach, we introduce a reference implementation, its underlying infrastructure and business scenarios.
KeywordsResponse Event Latent Semantic Analysis Event Service Solution Architecture Rule Service
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- 1.Anderson, M., Ball, M., Boley, H., Greene, S., Howse, N., Lemire, D., Mcgrath, S.: Racofi: A rule-applying collaborative filtering system. In: IEEE International Conference on Web Intelligence/Collaboration Agents, Halifax, Canada (2003)Google Scholar
- 2.Baraglia, R., Silvestri, F.: An Online Recommender System for Large Web Sites. In: IEEE/WIC/ACM International Conference on Web Intelligence, Beijing, China, pp. 199–205 (2004)Google Scholar
- 4.Jin, X., Zhou, Y., Mobasher, B.: Web usage mining based on probabilistic latent semantic analysis. In: 10th ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, pp. 197–205 (2004)Google Scholar
- 5.Luckham, D.: The Power of Events. Addison Wesley, Reading (2005)Google Scholar
- 6.Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective Personalization Based on Association Rule Discovery from Web Usage Data. In: 3rd ACM Workshop on Web Information and Data Management, Atlanta, GA, USA (2001)Google Scholar
- 7.Rozsnyai, S., Schiefer, J., Schatten, A.: Event Cloud - Searching for Correlated Business Events. In: 9th IEEE Conference on E-Commerce Technology and the 4th IEEE Conference on Enterprise Computing, E-Commerce and E-Services, Tokyo, Japan (2007)Google Scholar
- 8.Rozsnyai, S., Schiefer, J., Schatten, A.: Concepts and Models for Typing Events for Event-Based Systems. In: Inaugural International Conference on Distributed event-based systems, Toronto, Canada (2007)Google Scholar
- 9.Rozsnyai, S., Schiefer, J., Schatten, A.: Solution architecture for detecting and preventing fraud in real time. In: 2nd International Conference on Digital Information Management, Lyon, France, vol. 1, pp. 152–158 (2007)Google Scholar
- 10.Sarwar, B.M., Karypis, G., Konstan, J.A., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: 10th International World Wide Web Conference, Hongkong, China, pp. 285–295 (2001)Google Scholar
- 11.Schiefer, J., Rozsnyai, S., Schatten, A.: Event-Driven Rules for Sensing and Responding to Business Situations. In: Inaugural International Conference on Distributed event-based systems, Toronto, Canada (2007)Google Scholar
- 12.Schiefer, J., Seufert, A.: Management and Controlling of Time-Sensitive Business Processes with Sense & Respond. In: International Conference on Computational Intelligence for Modelling Control and Automation, Vienna (2005)Google Scholar
- 13.Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-T.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. ACM Special Interest Group on Knowledge Discovery and Data Mining Explorations 2(1) (2000)Google Scholar