User Profile Acquisition Approaches

This section discuss about the common traits or ideas observed in the three research topics. Although, each of the three articles discuss a unique idea, all of them are aimed at utilizing the web data to produce better results. Web data mining is a hot research topic in the current realm of big data. These papers discuss about the utilization of the valuable user generated data from the social media or the browser cookies to provide the best user experience in order to maintain the user interest in the company's product or to take effective decisions by an individual. All the three articles propose an idea to solution the problem stated, compared their results to the existing models and showed significant improvement.

Here we discuss about the common traits or ideas observed in the three research topics. Although, these three papers discuss about different ideas, they all fall under the web data mining domain. web data mining is a hot research topic in the current realm of big data. These papers discuss about the utilisation of the valuable user generated data from the social media or the the browser cookies to provide the best user experience in order to maintain the user interest in the company's product or to take effective decisions by the individual.

A website is used by different people for different uses. Each website can be evaluated for its usefulness by the general public based on certain criteria as discussed in this article.

In which rules are created from answers provided by users on questions about information usage and filtering behavior. Our system considers user’s profile (based on user’s weblog/navigation browsing history) and Domain Knowledge in order to perform personalized web search. Using Domain Knowledge, the system stores information about different domain/categories. Information obtained from User Profile is classified into these specified categories. The learning

The Internet?s leading advertising company, DoubleClick, Inc. compiled thorough information on the browsing routine of millions of users. They

However, targeted advertising has raised new questions on privacy since it must collect user’s information in order to publish advertisement. When a consumer visits a website, every page they view, the time spent on each page, the new pages they click on and how they interact with the server, allow browsers to collect that data. Analyzing from the technology used in behavioral targeting advertising, web browsing history will be tracked and sent to web server. In order to best select advertisements to display, data mining and machine learning theory will be implemented for analyzing users’ behavior (Korolova 2010).

If personalizing a customer’s Web site experience is a key success factor, then electronic profiling processes to track visitor Web site behavior are necessary. Do you agree or disagree with this statement? Explain your position.

Lots of persons interact everyday with web sites around the world. Massive amount of data are being generated and these information could be much respected to the company in the field of accepting Client’s behaviours. Web usage mining is relative independent, but not sequestered category, which mainly describes the techniques that discover the user's usage pattern and try to predict the user's behaviours. Web usage mining is the area of data mining which deals with the novelty and study of usage patterns with use of Web log data. Specifically web logs in direction to advance web based applications.

Information is the foundation of all the business and as they constantly learn about the customers, it can be the key to increase profit. There are different ways to gather customer information for example: software now that exists can report on the visitor traffic experienced by web pages they viewed and how many times they returned and so on. All these information will be stored in log

User profiling techniques have widely applied in various web search, user-adaptive software systems, web user identification, personalization, recommendation, e-market analysis, intelligent tutoring systems, intelligent agents, as well as personalized information retrieval and filtering.

Association rule mining can be used to extract patterns of a website visitors’ behavior. This data can be used to improve web marketing (e business)techniques or to improve the web surfing experience. Here we are applying association rule on web usage log file of an institution. We are using association rule as a interesting measures and verifying their values in two different period of time. We will see how this comparison brings extra important information about association rules generation and helps a webmaster make more and more accurate decisions about the website development and enhancements.

Guide4gadgets.com within the specified period of review had a total of 1,370 page views, 1,168 of this page views are unique page views. The home page had the highest page views with 436(31.82%) of the overall page views. The total exit rate for the website was 21.53% with the home page having the highest share of exit pages. The average time

The measures for the success in Web personalization is defined by the set of handy beliefs that have been verified in this research. It was empirically exhibited drawn from various literatures such as algorithmic performance and user’s satisfaction. The former literature pertaining on computational intelligent, whereas the later belongs to Information Systems (IS) studies. From literatures in AP, it shows that many algorithms have been developed to cater the success of a WPS. However, user’s satisfaction from IS theory is also important in defining the success. It ranges from various aspects such as acceptance of technology that can be found through TAM, Web quality (WEBQUAL), IS-success model and verified with user’s behaviors for predicting the intentions of use and adopt Web personalization via TPB framework.

Bincy S Kalloor, Shiji C.G [8], proposed customized multi-annotator approach in "A Survey on Data Annotation for Web Databases" in September 2014. In this paper display a customized annotation approach, first alters the data units on a result page into unmistakable social affairs, such that the data in the same get-together have the same which implies. By then for each social affair remark it from particular component and total the assorted annotations to expect a last annotation mark. Priyanka C. Ghegade, Prof. Vinod Wadane [17], describes personalized web search (PWS) is one of the active ongoing research field that related to the retrieval of the relevant web page results based on the user interest and preferences. ARUNPANDI.V, SUNDARAMOORHTY.S, VARADHARAJAN.E [18], proposed the personalization involve in collecting the user interests implicitly or explicitly called as user profile. Collecting the user interests in explicit manner is not so easy. Because the users are not interested to provide their interest to the server. So we go for other methods to create user profile in implicit manner.

Data is an ever increasing focus of schools in this country. The huge drawback however is the fact that the data that is collected is seldom delivered in a timely manner, or used effectively to increase student performance. Learner analytics is the study of vast amounts of data to help predict performance, and assist those students who might be struggling. “It has strong roots in a variety of fields, particularly business intelligence, web analytics, educational data mining and recommender systems.”[5]