Web Usage Classification and Clustering Approach for Web Search Personalization
25 Sep 2015-pp 376-383
TL;DR: An efficient and novel web search based on the individual classification and clustering method that classified the cluster data using frequent pattern mining and multilevel association rules for recurring relationship and cluster the web usage using Hierarchical methods with the navigating site and user interest for personalization.
Abstract: The increases in the information resources on the World Wide Web in search of the necessary information, as users navigate the Web with multiple sites. When user surfing the web which is a huge and complicated often miss their required searching pages. Web personalization is based on the Web usage logs of user's makes advantage of the knowledge required for the analysis of the content and structure of web sites promising to solve this problem by supporting one of the procedures. The search engine can affect the effectiveness of existing approaches, depending on the user profile, which is building more and more on the web pages or documents. In this paper, we propose an efficient and novel web search based on the individual classification and clustering method. The proposed approach classified the cluster data using frequent pattern mining and multilevel association rules for recurring relationship and cluster the web usage using Hierarchical methods with the navigating site and user interest for personalization. This approach process in advance to support the real time personalization and minimizes the cost reduction of preparation personalization resource in real time. The proposed approach is an effective personalization to the user's interest; in experimental research it has shown high precision measures.
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TL;DR: Results indicate that enhanced clustering methods, according to the new wiki-KNN based representation method in comparison with the baseline methods, show a significant improvement in WSRC and the new data representation scheme has enhanced the overall performance of clustering Methods.
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Book•
04 Dec 1998
TL;DR: This is the first textbook on formal concept analysis that gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially in data analysis and knowledge processing.
Abstract: From the Publisher:
This is the first textbook on formal concept analysis. It gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially in data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. Theory and graphical representation are thus closely coupled together. The mathematical foundations are treated thoroughly and illuminated by means of numerous examples.
4,757 citations
"Web Usage Classification and Cluste..." refers background in this paper
...User profile is characterized as a network concept, where each concept is about the theory marked the formal concept analysis (FCA)[10]....
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...User profile is characterized as a network concept, where each concept is about the theory marked the formal concept analysis (FCA)[10]....
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TL;DR: The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high.
Abstract: newsgroups carry a wide enough spread of messages to make most individuals consider Usenet news to be a high noise information resource. Furthermore, each user values a different set of messages. Both taste and prior knowledge are major factors in evaluating news articles. For example, readers of the rec.humor newsgroup, a group designed for jokes and other humorous postings, value articles based on whether they perceive them to be funny. Readers of technical groups, such as comp.lang.c11 value articles based on interest and usefulness to them—introductory questions and answers may be uninteresting to an expert C11 programmer just as debates over subtle and advanced language features may be useless to the novice. The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering. More formally, we determined the potential predictive utility for Usenet news was very high. The GroupLens project started in 1992 and completed a pilot study at two sites to establish the feasibility of using collaborative filtering for Usenet news [8]. Several critical design decisions were made as part of that pilot study, including:
2,657 citations
TL;DR: This article introduces the modules that comprise a Web personalization system, emphasizing the Web usage mining module, and presents a review of the most common methods that are used as well as technical issues that occur.
Abstract: Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented.
941 citations
"Web Usage Classification and Cluste..." refers background in this paper
...A typical search engine provides substantially similar results regardless of the intention of the user [1][2][3]....
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TL;DR: A novel technique to learn user profiles from users' search histories is proposed, which are then used to improve retrieval effectiveness in Web search.
Abstract: Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.
451 citations
"Web Usage Classification and Cluste..." refers background in this paper
...PERSONALIZATION Web search displays user interest using their personalized information of each user information retrieval [7]....
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TL;DR: This paper is a survey of recent work in the field of web usage mining for the benefit of research on the personalization of Web-based information services, focusing on the problems identified and the solutions that have been proposed.
Abstract: This paper is a survey of recent work in the field of web usage mining for the benefitof research on the personalization of Web-based information services. The essence of personalization is the adaptability of information systems to the needs of their users. This issue is becoming increasingly important on the Web, as non-expert users are overwhelmed by the quantity of information available online, while commercial Web sites strive to add value to their services in order to create loyal relationships with their visitors-customers. This article views Web personalization through the prism of personalization policies adopted by Web sites and implementing a variety of functions. In this context, the area of Web usage mining is a valuable source of ideas and methods for the implementation of personalization functionality. We therefore present a survey of the most recent work in the field of Web usage mining, focusing on the problemsthat have been identified and the solutions that have been proposed.
426 citations
Additional excerpts
...Many traditional methodologies[15][16] are proposed based on the location...
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