958 research outputs found
Web Mining for Web Personalization
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\u27s 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
Personalised online sales using web usage data mining
Practically every major company with a retail operation has its own web site and online sales facilities. This paper describes a toolset that exploits web usage data mining techniques to identify customer Internet browsing patterns. These patterns are then used to underpin a personalised product recommendation system for online sales. Within the architecture, a Kohonen neural network or self-organizing map (SOM) has been trained for use both offline, to discover user group profiles, and in real-time to examine active user click stream data, make a match to a specific user group, and recommend a unique set of product browsing options appropriate to an individual user. Our work demonstrates that this approach can overcome the scalability problem that is common among these types of system. Our results also show that a personalised recommender system powered by the SOM predictive model is able to produce consistent recommendations
A Survey on Web Usage Mining, Applications and Tools
World Wide Web is a vast collection of unstructured web documents like text, images, audio, video or Multimedia content. As web is growing rapidly with millions of documents, mining the data from the web is a difficult task. To mine various patterns from the web is known as Web mining. Web mining is further classified as content mining, structure mining and web usage mining. Web usage mining is the data mining technique to mine the knowledge of usage of web data from World Wide Web. Web usage mining extracts useful information from various web logs i.e. users usage history. This is useful for better understanding and serve the people for better web applications. Web usage mining not only useful for the people who access the documents from the World Wide Web, but also it useful for many applications like e-commerce to do personalized marketing, e-services, the government agencies to classify threats and fight against terrorism, fraud detection, to identify criminal activities, the companies can establish better customer relationship and can improve their businesses by analyzing the people buying strategies etc. This paper is going to explain in detail about web usage mining and how it is helpful. Web Usage Mining has seen rapid increase towards research and people communities
Combination of web usage, content and structure information for diverse web mining applications in the tourism context and the context of users with disabilities
188 p.This PhD focuses on the application of machine learning techniques for behaviourmodelling in different types of websites. Using data mining techniques two aspects whichare problematic and difficult to solve have been addressed: getting the system todynamically adapt to possible changes of user preferences, and to try to extract theinformation necessary to ensure the adaptation in a transparent manner for the users,without infringing on their privacy. The work in question combines information of differentnature such as usage information, content information and website structure and usesappropriate web mining techniques to extract as much knowledge as possible from thewebsites. The extracted knowledge is used for different purposes such as adaptingwebsites to the users through proposals of interesting links, so that the users can get therelevant information more easily and comfortably; for discovering interests or needs ofusers accessing the website and to inform the service providers about it; or detectingproblems during navigation.Systems have been successfully generated for two completely different fields: thefield of tourism, working with the website of bidasoa turismo (www.bidasoaturismo.com)and, the field of disabled people, working with discapnet website (www.discapnet.com)from ONCE/Tecnosite foundation
A new technique for intelligent web personal recommendation
Personal recommendation systems nowadays are very important in web applications
because of the available huge volume of information on the World Wide Web, and the
necessity to save usersâ time, and provide appropriate desired information, knowledge,
items, etc. The most popular recommendation systems are collaborative filtering systems,
which suffer from certain problems such as cold-start, privacy, user identification, and
scalability. In this thesis, we suggest a new method to solve the cold start problem taking
into consideration the privacy issue. The method is shown to perform very well in
comparison with alternative methods, while having better properties regarding user privacy.
The cold start problem covers the situation when recommendation systems have not
sufficient information about a new userâs preferences (the user cold start problem), as well
as the case of newly added items to the system (the item cold start problem), in which case
the system will not be able to provide recommendations. Some systems use usersâ
demographical data as a basis for generating recommendations in such cases (e.g. the
Triadic Aspect method), but this solves only the user cold start problem and enforces userâs
privacy. Some systems use usersâ âstereotypesâ to generate recommendations, but
stereotypes often do not reflect the actual preferences of individual users. While some other
systems use userâs âfilterbotsâ by injecting pseudo users or bots into the system and consider
these as existing ones, but this leads to poor accuracy.
We propose the active node method, that uses previous and recent usersâ browsing targets
and browsing patterns to infer preferences and generate recommendations (node
recommendations, in which a single suggestion is given, and batch recommendations, in
which a set of possible target nodes are shown to the user at once). We compare the active
node method with three alternative methods (Triadic Aspect Method, NaĂŻve Filterbots
Method, and MediaScout Stereotype Method), and we used a dataset collected from online
web news to generate recommendations based on our method and based on the three
alternative methods. We calculated the levels of novelty, coverage, and precision in these
experiments, and we found that our method achieves higher levels of novelty in batch
recommendation while achieving higher levels of coverage and precision in node
recommendations comparing to these alternative methods. Further, we develop a variant of
the active node method that incorporates semantic structure elements. A further
experimental evaluation with real data and users showed that semantic node
recommendation with the active node method achieved higher levels of novelty than nonsemantic
node recommendation, and semantic-batch recommendation achieved higher levels
of coverage and precision than non-semantic batch recommendation
An Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents
The Proposed System Analyses the scopes introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the 201C;noise.201D; Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items. We propose an advanced data mining method using SVD that combines both tag and value similarity, item and user preference. SVD automatically extracts data from query result pages by first identifying and segmenting the query result records in the query result pages and then aligning the segmented query result records into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the query result records based on user preferences, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested-structure that may exist in the query result records
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