Research that Adapts Information Retrieval Based on a User Profile

Launched on 15 January 2001, Wikipedia is a free encyclopedia that uses the web platform for online users to access. Boasting with over 26 million pieces of writing in 285 languages, Wikipedia has transformed to be a giant in the field of search engines optimization technology. The open source concept that it rides have made it cheap to access and a better choice for many online users. This is especially among the users who find it cumbersome to follow prolonged registration processes to access information on the internet. Any search term queried on the Google™ home page search engine will definitely give a hit from the Wikipedia site, and if not present, a prompt will request the user to create a page for such a term. In this way,

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.

With the advent of computer technology in 1990’s the need to search large databases was increasingly becoming vital. The search engines prior to PageRank had limitations, the then most widely used algorithm used text based indexes to provide search results on World Wide Web however had limitations of improper search results as the logic used by the search engines looked at the number of occurrences of the search word in webpage which sometimes resulted in improper search results. Another technique used during the time was based on variations of standard vector space model – i.e. search based on how recent the webpage was updated and/or how close the search terms are to the

As a student that values the ability to make my own choices and opportunities to conduct research, UChicago appeals to my sense of learning. Unlike other universities, UChicago’s Department of Computer Science provides some of the best education in the world and allows students to build strength in an additional field due to the program’s flexibility. Not only that, UChicago’s Center for Scholarly Advancement encourages undergraduate research and would allow me to continue the scientific research that I am currently conducting, as well as allowing me to be mentored by great professors who are researching similar topics. In particular, Professor Ben Zhao and Professor Heather Zheng’s research on data-driven models of user behavior appeals to

When searching on the Internet, one may find it difficult sometimes to know where to start. With the seemingly limitless amount of information, one should use the resource suitable for the searcher's needs and tastes. Comparing different factors like databases, directory types, strengths and weaknesses of two search engines, such as Yahoo! and Lycos, can provide an advantage to someone looking for a starting block.

When you engage and visit our Site, we may collect and store some or all of the following; the IP address that you used to access the Site, date and time, IP address of the web site from which you linked to the Site, names of files and words searched on our Site, pages and items clicked on our site, the browser you used, and operating system used. This information is gathered and collected to measure the number of visitors we have on our site, the various sections of our site they engage, which is done to identify the performance of the system and

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

Gmail, a subsidiary of Google is able to track consumer data with every click that a consumer makes with their mouse as the cruise the internet. Market research firms collect data daily about consumers. They then make note of the buying and internet surfing trends of consumers. They use cluster analysis by putting the clusters or groups of consumers with similar trends together and then marketing new products or services to them, (Downes, 2012).

People who require information and furthermore, specific information for example “health” use special sites on the web, termed internet search engines that assist in retrieving stored information on other sites. (Franklin, 2000) Many search engines are available and some are designed for specific purposes; the two most popular all-purpose search engines are Google and Yahoo. Medical search engines are distinctively designed for

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).

Social tagging, which originally emerged as means for users to describe, organize and share content, forming groups known as “folksonomies,” has challenged the traditional idea of organizing knowledge within information systems, raising questions whether tags and folksonomies might improve information retrieval, thus bridging the gap between lay persons and builders (Smith 136-139; Lee and Schiyer 1747-1748; Rolla 174-175; 182-183). In fact, folksonomies have been proposed an alternative way to organize and find information, such as Park, who applying the “information foraging theory,” proposed that since users naturally collect and evaluate results, folksonomies can help facilitate the discovery of information through tag- browsing, allowing users to find related tags classified by others(Smith 137; Park 515-518; 521-522). Yet, despite the uniqueness of this model, there could be shortcomings, because while tags serving as categories for browsing might be a good idea for smaller folksonomies, it would be difficult for a user to find all relevant items (recall) within a large folksonomy of thousands items, especially if the tags are broad, not connected by multiple terms, and the user is looking for specific information (Unit 1). Instead, tags would be more effective as indexing terms, something that has been explored for viability against library systems, such as in OPACS.

Abstract—Web searching engine are mostly used for finding certain information among a huge amount of data in a minimum amount of period. Profile based personalized web search (PWS) has determine their effectiveness in improving the quality of various internet search technique. We study security of privacy protection for PWS applications that as hierarchical user profiles. In this paper we can implement the PWS system called UPS that can flexibly create profiles by queries while regarding user definite privacy requirements. Our runtime generalization goals at striking a level between two predictive metrics that evaluate the effectiveness of personalization and the privacy risk of exposing the generalized profile. We implemented this system with greedy algorithms, for runtime profile generalization also provides a runtime prediction mechanism for determining whether personalizing a query is useful. Extensive experiments prove the effectiveness of our system. The experimental results also reveal that Greedy algorithms significantly outperform in terms of effectiveness.

The paper Semantic Web talks about how the machine can be more intelligent when accessing web data. It involves making a machine understand the semantics of the data present on the web and also making them understand the human perspective. The research first tries the basic requirement outlines for the semantic web. Like the creation of ontologies and different relationship using which a machine can learn the context. The paper then discusses the creation of agents which takes advantage of the semantic web technology and gives a more personalized output to the user. In the paper the author has taken few real life examples is setting the benchmark requirements of the semantic web.

It may include demographic information, e.g., name, age, country, education level, etc. and should capture the behavior (patterns, goals, interesting topics, etc.) of a user shows when interacting with the Web. User modeling is defined as the process of gathering information about user’s interests, constructing, maintaining and using user profiles. For example in e-business systems, it captures online users’ characteristics, knows online users, provides customized products and services, and therefore improve user satisfaction. There are two approaches in user modeling process. These are the generation of an initial user profile for a new user and the continuous update of the profile information to adapt user’s changing preferences, interests and needs. A major challenge within user profiling is how user profiles can be constructed that accurately reflects users’ preferences.