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Titlebook: Advances in Web Mining and Web Usage Analysis; 8th International Wo Olfa Nasraoui,Myra Spiliopoulou,Brij Masand Conference proceedings 2007

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发表于 2025-3-21 17:36:33 | 显示全部楼层 |阅读模式
期刊全称Advances in Web Mining and Web Usage Analysis
期刊简称8th International Wo
影响因子2023Olfa Nasraoui,Myra Spiliopoulou,Brij Masand
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Advances in Web Mining and Web Usage Analysis; 8th International Wo Olfa Nasraoui,Myra Spiliopoulou,Brij Masand Conference proceedings 2007
影响因子This book contains the postworkshop proceedings with selected revised papers from the 8th international workshop on knowledge discovery from the Web, WEBKDD 2006. The WEBKDD workshop series has taken place as part of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) since 1999. The discipline of data mining delivers methodologies and tools for the an- ysis of large data volumes and the extraction of comprehensible and non-trivial insights from them. Web mining, a much younger discipline, concentrates on the analysisofdata pertinentto the Web.Web mining methods areappliedonusage data and Web site content; they strive to improve our understanding of how the Web is used, to enhance usability and to promote mutual satisfaction between e-business venues and their potential customers. Inthelastfewyears,theinterestfortheWebasamediumforcommunication, interaction and business has led to new challenges and to intensive, dedicated research.Many ofthe infancy problems in Web mining have been solvedby now, but the tremendous potential for new and improved uses, as well as misuses, of the Web are leading to new challenges. ThethemeoftheWebKDD2006workshopwas“Kno
Pindex Conference proceedings 2007
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Incorporating Usage Information into Average-Clicks Algorithm,ser’s intuition of distance better than the traditional measure of clicks between two pages. Average-Clicks however assumes that the probability of the user following any link on a web page is the same and gives equal weights to each of the out-going links. In our method “Usage Aware Average-Clicks”
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Nearest-Biclusters Collaborative Filtering with Constant Values,hbor CF is based either on common user or item similarities, to form the user’s neighborhood. The effectiveness of the aforementioned approaches would be augmented, if we could combine them. In this paper, we use biclustering to disclose this duality between users and items, by grouping them in both
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How to Define Searching Sessions on Web Search Engines, searchers. We compare three methods for defining sessions using: 1) Internet Protocol address and cookie; 2) Internet Protocol address, cookie, and a temporal limit on intra-session interactions; and 3) Internet Protocol address, cookie, and query reformulation patterns. Research results shows that
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Incorporating Concept Hierarchies into Usage Mining Based Recommendations,vided by a recommendation system. Resources like Google Directory, Yahoo! Directory and web-content management systems attempt to organize content conceptually. Most recommendation models are limited in their ability to use this domain knowledge. We propose a novel technique to incorporate the conce
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A Random-Walk Based Scoring Algorithm Applied to Recommender Engines,lized product suggestions by extracting knowledge from the previous users’ interactions. In this paper, we present “ItemRank”, a random–walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top–rank items to potentially interes
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Towards a Scalable ,NN CF Algorithm: Exploring Effective Applications of Clustering,sers benefit as they are able to find items of interest from an unmanageable number of available items. On the other hand, e-commerce sites that employ recommender systems can increase sales revenue in at least two ways: a) by drawing customers’ attention to items that they are likely to buy, and b)
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