Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Web personalization integrating content semantics and navigational patterns
Proceedings of the 6th annual ACM international workshop on Web information and data management
Web path recommendations based on page ranking and Markov models
Proceedings of the 7th annual ACM international workshop on Web information and data management
Usage-Based PageRank for Web Personalization
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
A recommendation framework for remote sensing images by spatial relation analysis
Journal of Systems and Software
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Nowadays, the well-known search engines, such as Google, Yahoo, MSN, etc, have provided the users with good search results based on special search strategies. However there still exist some problems unsolved for traditional search engines, including: 1) the gap between user’s intention and searched results is not easy to narrow down under the global search space, and 2) user’s interested pages hidden in the local website are not associated with the search results. To deal with such problems, in this paper, we propose a novel approach for personalized page ranking and recommendation by integrating association mining and PageRank so as to meet user’s search goals. Moreover, by mining the users’ browsing behaviors, we can successfully bridge the gap between global search results and local preferences. The effectiveness of our proposed approach was verified through experimental evaluations.