Publication Type : Conference Paper
Publisher : 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)
Source : 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), IEEE, Coimbatore, India (2017)
Url : https://ieeexplore.ieee.org/document/8524198
ISBN : 9781509066216
Keywords : adaptive web personalization model, Computer architecture, Data mining, dynamic users, Information Retrieval, Navigation, Navigational pattern, Performance evaluation, Personalization, Prediction algorithms, Recommendation system, time-driven adaptive web personalization system, Timing, timing attributes, Training, web data, Web design, web designers, Web mining, Web pages, Web services, Web usage Mining
Campus : Amritapuri
School : School of Engineering
Department : Computer Science and Engineering, Computer Science
Year : 2017
Abstract : Web data has been increased to many folds in the recent past. Study of web data for collecting relevant information is indispensable for providing efficient web services. However, web designers are facing difficulty in organizing their site to meet the user's demands. Web personalization solves this problem to a certain extent. This paper presents an adaptive web personalization model which classifies the user's browsing behavior and predicts their interested areas. The proposed model is designed with a two tier architecture in which the first tier clusters the user's navigation patterns, according to the timing attributes extracted from the log file. In the second tier, the user's interested areas are predicted using a classifier. The proposed model is validated through experiments.
Cite this Research Publication : P. Das and Sajeev, G. P., “Time- Driven Adaptive Web Personalization System for Dynamic Users”, in 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 2017.