Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Effects of Session Representation Models on the Performance of Web Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
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In this paper, we propose a new hybrid recommendation model for web users which is based on multiple recommender systems working in parallel. With the rapid growth of the World Wide Web (www), it becomes a critical issue to find useful information from the Internet. Web recommender systems help people make decisions in this complex information space where the volume of information available to them is huge. Recently, a number of approaches have been developed to extract the user behavior from her navigational path and predict her next request as she visits Web pages. Some of these approaches are based on non-sequential models such as association rules and clustering, and some are based on sequential patterns. In this paper, we present a hybrid recommender model which combines the results of multiple recommender systems in an effective way. We have conducted a detailed evaluation on four different web usage data. Our results show that combining recommendation algorithms effectively leads a better recommendation accuracy. The experimental evaluation shows that our method can achieve a better prediction accuracy compared to standard recommendation systems while still guaranteeing competitive time requirements.