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- W2183625218 abstract "Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users’ interactions. Currently much research is focus on web page recommendations using sequential pattern mining techniques. Sequential access pattern mining discovers interesting and frequent user access patterns from web logs. Most of the previous studies have adopted Apriori-like sequential pattern mining techniques, which faced the problem on requiring expensive multiple scans of databases. In this paper a traditional sequential pattern mining algorithm called prefixspan is modified by incorporating two measures such as, spending time and recent view. Then, the weighted sequential patterns are utilized to construct the recommendation model using the Patricia trie-based tree structure. Finally, the recommendation of the current users is done with the help of markov model." @default.
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- W2183625218 date "2012-01-01" @default.
- W2183625218 modified "2023-09-25" @default.
- W2183625218 title "Finding of Weighted Sequential Web Access Patterns for Effective Web Page Recommendations" @default.
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