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Titlebook: Web Recommendations Systems; K. R. Venugopal,K. C. Srikantaiah,Sejal Santosh Ni Book 2020 Springer Nature Singapore Pte Ltd. 2020 Web reco

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發(fā)表于 2025-3-30 11:58:24 | 只看該作者
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發(fā)表于 2025-3-30 13:56:55 | 只看該作者
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發(fā)表于 2025-3-30 17:56:43 | 只看該作者
Construction of Topic Directories Using Levenshtein Similarity Weight,directory is one of the major challenges faced by human-based topic directories due to the rapid pace of growth of the WWW and also the presence of a large number of categories. So, the mapping of new pages onto categories by human experts is an expensive process. Hence, the automation of this proce
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發(fā)表于 2025-3-30 22:32:16 | 只看該作者
Related Search Recommendation with User Feedback Session,es relevant to their search because of adequate knowledge about the domain. Therefore, the input queries are normally ambiguous and short. Query suggestion is a method to recommend queries related to the user input query that helps them to locate their required information more precisely. It helps t
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發(fā)表于 2025-3-31 01:58:44 | 只看該作者
Related Search Recommendation with User Feedback Session,es relevant to their search because of adequate knowledge about the domain. Therefore, the input queries are normally ambiguous and short. Query suggestion is a method to recommend queries related to the user input query that helps them to locate their required information more precisely. It helps t
56#
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Web Page Recommendations Based on User Session Graph,In this chapter, Web page recommendation method is presented by constructing User Session Graph using user sessions from the navigation log. The node represents Web pages and weight on the edge is calculated by the number of times the Web pages present in the sessions. . is solved by computing co-oc
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發(fā)表于 2025-3-31 19:19:44 | 只看該作者
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發(fā)表于 2025-4-1 01:29:00 | 只看該作者
Advertisement Recommendations Using Expectation Maximization,sers’ demand is identified, advertisers can target those users with an appropriate query. In this chapter, predicting conversion in advertising using expectation–maximization [PCAEM] model is proposed to provide an influence of their advertising campaigns to the advertisers by understanding hidden t
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