Identification of User Search Targets Using Feed Backs 1

Abstract Different users may have different search objectives and goals for a huge and confusing search item. The search engine performance can be improved by identifying and analyzing the search goals . In this paper, we propose a studied the approach to identify the user search goals by analyzing search engine query logs. The search goals of different users by clustering the proposed feedback from the search sessions.. to get the best results it is necessary to capture different user search goals. These user goals are nothing but information on different aspects of a query that different users want to obtain. The judgment and analysis of user search goals can be improved by the relevant result obtained from search engine and user's feedback. Here, feedback sessions are used to discover different user search goals based on series of both clicked and un clicked URL's. The pseudo-documents are generated to better represent feedback sessions which can reflect the information need of user. With this the original search results are restructured and to evaluate the performance of restructured search results, classified average precision is used. Keywords Search Goals, Feedback Sessions, Pseudo-Documents I. Introduction Web mining is one of the applications of data mining techniques to discover knowledge from the web. In web search, users are submitted queries to the search engines to get relevant information. But many search engines results are not informative and failed to produce results according to the user search goals. Users are usually giving some vague keywords representing their interests in their minds. Such keywords do not match with the results produced by the search engines. Many works about user search goals analysis should be carried out. Some users give ambiguous queries to the search engines they get mostly the irrelevant results. User search goals are classified as Navigational and Informational, the queries that seek a single website or webpage and queries that reflect the intent of the user to perform a particular transaction respectively. Many related works have been carried out according to the web search applications and the user search goals. In previous works, clustering is done on a set of top ranked results. The user search logs information is not analyzed and the feedback sessions are not considered. Analyzing the clicked URLS only from the web search logs. They only identify whether a pair of queries belong to the same goal or mission and does not care about what the goal is in detail. Semantic based web search for a particular query and the similarity between the words are carried out. Various algorithms such as star clustering algorithm, k-means clustering algorithm are used for clustering the pseudo documents but it also does not cluster the relevant information according to the user search goals. In clustering the cluster labels discovered are also not informative. User search goal is the information on different aspects of a query that users wants to obtain. Information need is a user's desire to obtain the relevant information to satisfy his need. To cluster web search results, the URLs are analyzed by extracting the titles and snippets. But all those works produced noisy results and does not obtain the user search goals precisely. When more irrelevant and relevant results are produced by the search engines it is tim

A distributed solution to software reuse

Reuse can be applied to all stages of the software lifecycle to enhance quality and to shorten time of completion for a project. During the phases of design and implementation are some examples of where reuse can be applied, but one frequent obstruction to development is the building of and the identifying of desirable components. This can be costly in the short term but an organisation can gain the profits of applying this scheme if they are seeking long-term goals. Web services are a recent development in distributed computing. This thesis combines the two research areas to produce a distributed solution to software reuse that displays the advantages of distributed computing within a reuse system. This resulted in a web application with access to web services that allowed two different formats of component to be inserted into a reuse repository. These components were searchable by keywords and the results are adjustable by the popularity of a component’s extraction from the system and by user ratings of it; this improved the accuracy of the search. This work displays the accuracy, usability, and speed of this system when tested with five undergraduate and five postgraduate students

Enhancing Undergraduate AI Courses through Machine Learning Projects

It is generally recognized that an undergraduate introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core topics that are typically covered. The paper presents work funded by the National Science Foundation to address this problem and to enhance the student learning experience in the course. Our work involves the development of an adaptable framework for the presentation of core AI topics through a unifying theme of machine learning. A suite of hands-on semester-long projects are developed, each involving the design and implementation of a learning system that enhances a commonly-deployed application. The projects use machine learning as a unifying theme to tie together the core AI topics. In this paper, we will first provide an overview of our model and the projects being developed and will then present in some detail our experiences with one of the projects – Web User Profiling which we have used in our AI class

Oblivion: Mitigating Privacy Leaks by Controlling the Discoverability of Online Information

Search engines are the prevalently used tools to collect information about individuals on the Internet. Search results typically comprise a variety of sources that contain personal information -- either intentionally released by the person herself, or unintentionally leaked or published by third parties, often with detrimental effects on the individual's privacy. To grant individuals the ability to regain control over their disseminated personal information, the European Court of Justice recently ruled that EU citizens have a right to be forgotten in the sense that indexing systems, must offer them technical means to request removal of links from search results that point to sources violating their data protection rights. As of now, these technical means consist of a web form that requires a user to manually identify all relevant links upfront and to insert them into the web form, followed by a manual evaluation by employees of the indexing system to assess if the request is eligible and lawful. We propose a universal framework Oblivion to support the automation of the right to be forgotten in a scalable, provable and privacy-preserving manner. First, Oblivion enables a user to automatically find and tag her disseminated personal information using natural language processing and image recognition techniques and file a request in a privacy-preserving manner. Second, Oblivion provides indexing systems with an automated and provable eligibility mechanism, asserting that the author of a request is indeed affected by an online resource. The automated ligibility proof ensures censorship-resistance so that only legitimately affected individuals can request the removal of corresponding links from search results. We have conducted comprehensive evaluations, showing that Oblivion is capable of handling 278 removal requests per second, and is hence suitable for large-scale deployment

An investigation into the application of Claims Analysis to evaluate usability of a digital library interface

There is a need for tools that help developers evaluate the usability of digital library interfaces. The potential for using Claims Analysis to help developers in this way has been investigated in three linked case studies. The first explored the design rationale of an existing design with its developers. This showed that they had considered positive consequences for novice uses but that they found it difficult to identify negative effects. The second study explored the detailed design of an add-on feature. A scenario and sample claims were introduced to evaluate exploratory use within an action cycle of planning, execution and evaluation. This framework provided an effective stimulus to enable the developers to evaluate the design and explore opportunities for redesign. Finally, some novice users explored the digital library and the findings from this were used to validate a user scenario and claims

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

Recent Developments in Cultural Heritage Image Databases: Directions for User-Centered Design

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