標(biāo)題: Titlebook: Advances in Web Mining and Web Usage Analysis; 7th International Wo Olfa Nasraoui,Osmar Za?ane,Philip S. Yu Conference proceedings 2006 Spr [打印本頁] 作者: NO610 時(shí)間: 2025-3-21 18:01
書目名稱Advances in Web Mining and Web Usage Analysis影響因子(影響力)
書目名稱Advances in Web Mining and Web Usage Analysis影響因子(影響力)學(xué)科排名
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書目名稱Advances in Web Mining and Web Usage Analysis網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Web Mining and Web Usage Analysis被引頻次
書目名稱Advances in Web Mining and Web Usage Analysis被引頻次學(xué)科排名
書目名稱Advances in Web Mining and Web Usage Analysis年度引用
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書目名稱Advances in Web Mining and Web Usage Analysis讀者反饋
書目名稱Advances in Web Mining and Web Usage Analysis讀者反饋學(xué)科排名
作者: 固定某物 時(shí)間: 2025-3-21 21:35 作者: Decimate 時(shí)間: 2025-3-22 01:32
Volodymyr Ivanov,Viktor Stabnikovndividual patterns. Semantics are used as well as learned in this process. fAP-IP is implemented as an extension of Gaston (Nijssen & Kok, 2004), and it is complemented by the AP-IP visualization tool that allows the user to navigate through detail-and-context views of taxonomy context, pattern cont作者: Condescending 時(shí)間: 2025-3-22 05:43 作者: 斗志 時(shí)間: 2025-3-22 11:14
Using and Learning Semantics in Frequent Subgraph Mining,ndividual patterns. Semantics are used as well as learned in this process. fAP-IP is implemented as an extension of Gaston (Nijssen & Kok, 2004), and it is complemented by the AP-IP visualization tool that allows the user to navigate through detail-and-context views of taxonomy context, pattern cont作者: 植物群 時(shí)間: 2025-3-22 16:56
Data Sparsity Issues in the Collaborative Filtering Framework,assification/regression task, virtually any supervised learning algorithm (such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the other hand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative 作者: 神秘 時(shí)間: 2025-3-22 18:08
https://doi.org/10.1007/978-3-319-04429-3lustering in order to provide a concise understanding of the underlying trends. We discuss our recent techniques which use micro-clustering in order to diagnose the changes in the underlying data. We also discuss the extension of this method to text and categorical data sets as well community detection in graph data streams.作者: 無動(dòng)于衷 時(shí)間: 2025-3-22 21:30 作者: facetious 時(shí)間: 2025-3-23 04:36 作者: 割公牛膨脹 時(shí)間: 2025-3-23 09:14
Mining Significant Usage Patterns from Clickstream Data,ing Web log data provided by J.C.Penney demonstrate that SUPs of different types of customers are distinguishable and interpretable. This technique is particularly suited for analysis of dynamic websites.作者: 一起平行 時(shí)間: 2025-3-23 13:38
Overcoming Incomplete User Models in Recommendation Systems Via an Ontology, a single individual’s preferences and this ontology performs better than collaborative filtering, with the greatest differences when little data about the user is available. This points the way to how proper inductive bias can be used for significantly more powerful recommender systems in the future.作者: nocturnal 時(shí)間: 2025-3-23 16:59
Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation,ers with similar tastes and show that such an attack can be highly successful against both user-based and item-based collaborative filtering. We also introduce a detection model that can significantly decrease the impact of this attack.作者: BYRE 時(shí)間: 2025-3-23 18:57
On Clustering Techniques for Change Diagnosis in Data Streams,lustering in order to provide a concise understanding of the underlying trends. We discuss our recent techniques which use micro-clustering in order to diagnose the changes in the underlying data. We also discuss the extension of this method to text and categorical data sets as well community detection in graph data streams.作者: GRUEL 時(shí)間: 2025-3-24 00:24
Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks,ges will be determined implicitly, without directly asking the user. Experimental results indicate that our personalized ranking methods, when used with a popular search engine, can yield more potentially interesting web pages for individual users.作者: fluoroscopy 時(shí)間: 2025-3-24 03:43 作者: BLA 時(shí)間: 2025-3-24 08:43
Reg Thomas BSc (Hons), FCIOB, ACIArb, MBIM a single individual’s preferences and this ontology performs better than collaborative filtering, with the greatest differences when little data about the user is available. This points the way to how proper inductive bias can be used for significantly more powerful recommender systems in the future.作者: 反應(yīng) 時(shí)間: 2025-3-24 13:00 作者: 免費(fèi) 時(shí)間: 2025-3-24 16:03 作者: Progesterone 時(shí)間: 2025-3-24 19:11
Pui Ting Chow,Sai On Cheung,Ka Ying Chanow, as opposed to what is popular among other users. The approach is usersensitive in that it adopts a ‘model of learning’ whereby the user’s context is dynamically interpreted as they browse and then leveraging that information to improve our recommendations.作者: 多產(chǎn)子 時(shí)間: 2025-3-25 01:21 作者: HEDGE 時(shí)間: 2025-3-25 06:20 作者: 分散 時(shí)間: 2025-3-25 10:59
Adaptive Web Usage Profiling,nt incremental update of usage profiles. An impact factor is defined whose value can be used to decide the need for recompilation. The results from extensive experiments on a large real dataset of web logs show that the proposed maintenance technique, with considerably reduced computational costs, is almost as good as complete remodeling.作者: agonist 時(shí)間: 2025-3-25 14:45
https://doi.org/10.1007/11891321Web access seq; association rule mining; data mining; filtering; graph mining; knowledge; knowledge discov作者: stressors 時(shí)間: 2025-3-25 19:39
978-3-540-46346-7Springer-Verlag Berlin Heidelberg 2006作者: 小丑 時(shí)間: 2025-3-25 23:07 作者: 使熄滅 時(shí)間: 2025-3-26 04:12
Volodymyr Ivanov,Viktor Stabnikove Patterns (SUP) is proposed and used to acquire significant “user preferred navigational trails”. The technique uses pipelined processing phases including sub-abstraction of sessionized Web clickstreams, clustering of the abstracted Web sessions, concept-based abstraction of the clustered sessions,作者: Axon895 時(shí)間: 2025-3-26 06:33
Volodymyr Ivanov,Viktor Stabnikovph structures in mining, however, should also take into account that it is essential to integrate background knowledge into the analysis, and that patterns must be studied at different levels of abstraction. To capture these needs, we propose to use taxonomies in mining and to extend frequency / sup作者: 脆弱吧 時(shí)間: 2025-3-26 08:53
Reg Thomas BSc (Hons), FCIOB, ACIArb, MBIMe weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction models. We propose a new method that extends the utility model and assumes that the structure of user preferences follows an ontology of product attributes. Using the data of the MovieLens system, w作者: 秘方藥 時(shí)間: 2025-3-26 13:14 作者: PRO 時(shí)間: 2025-3-26 17:26 作者: 頂點(diǎn) 時(shí)間: 2025-3-26 21:57 作者: 移植 時(shí)間: 2025-3-27 04:35 作者: 低三下四之人 時(shí)間: 2025-3-27 07:29 作者: BRIBE 時(shí)間: 2025-3-27 09:28
Conceptualising Construction Disputesly designed to serve all users, without considering the interests of individual users. We propose a method to (re)rank the results from a search engine using a learned user profile, called a user interest hierarchy (UIH), from web pages that are of interest to the user. The user’s interest in web pa作者: 表主動(dòng) 時(shí)間: 2025-3-27 14:12
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/150175.jpg作者: 暖昧關(guān)系 時(shí)間: 2025-3-27 18:59
Mining Significant Usage Patterns from Clickstream Data,e Patterns (SUP) is proposed and used to acquire significant “user preferred navigational trails”. The technique uses pipelined processing phases including sub-abstraction of sessionized Web clickstreams, clustering of the abstracted Web sessions, concept-based abstraction of the clustered sessions,作者: 肌肉 時(shí)間: 2025-3-28 00:15 作者: Eosinophils 時(shí)間: 2025-3-28 04:59
Overcoming Incomplete User Models in Recommendation Systems Via an Ontology,e weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction models. We propose a new method that extends the utility model and assumes that the structure of user preferences follows an ontology of product attributes. Using the data of the MovieLens system, w作者: 管理員 時(shí)間: 2025-3-28 08:12
Data Sparsity Issues in the Collaborative Filtering Framework, greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques – collaborative filtering. Apart from giving an overview of collaborative filtering ap作者: QUAIL 時(shí)間: 2025-3-28 12:14 作者: Desert 時(shí)間: 2025-3-28 14:48
Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation,e open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers can easily introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. Our research in secure personalization is examining a range of attack models, 作者: 排名真古怪 時(shí)間: 2025-3-28 20:44
Adaptive Web Usage Profiling,mendation of pages, pre-fetching of pages, etc. While browsing behavior changes dynamically over time, many web usage modeling techniques are static due to prohibitive model compilation times and also lack of fast incremental update mechanism. However, profiles have to be maintained so that they dyn作者: Antecedent 時(shí)間: 2025-3-29 02:34 作者: Tractable 時(shí)間: 2025-3-29 03:59
Personalized Search Results with User Interest Hierarchies Learnt from Bookmarks,ly designed to serve all users, without considering the interests of individual users. We propose a method to (re)rank the results from a search engine using a learned user profile, called a user interest hierarchy (UIH), from web pages that are of interest to the user. The user’s interest in web pa作者: Conclave 時(shí)間: 2025-3-29 09:35
