An Algorithm to Use Feedback on Viewed Documents to Improve Web Query (original) (raw)
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Web Information Systems and Technologies, 2006
This paper presents an algorithm to improve a web search query based on the feedback on the viewed documents. A user who is searching for information on the Web marks the retrieved (viewed) documents as relevant or irrelevant to further expose the information needs expressed in the original query. A new web search query matching this improved understanding of the user's information needs is synthesized from these text documents. The methodology provides a way for creating web search query that matches the user's information need even when the user may have difficulty in doing so directly due to lack of experience in the query design or lack of familiarity of the search domain. A user survey has shown that the algorithmically formed query has recall coverage and precision characteristics better than those achieved by the experienced human web searchers.
A Survey on Relevance Feedback for Information Retrieval Based on User Query
— Users of online web engines frequently think that it's hard to express their requirement for information as a query. Be that as it may, if the user can distinguish cases of the sort of records they require, then they can utilize a system known as relevance feedback. Relevance feedback covers a scope of methods planned to enhance a user's inquiry and encourage recovery of information important to a user's information require. In this paper we review relevance feedback strategies. We presented both procedures, in which the framework changes the user's inquiry, and intuitive strategies, in which the user has control over question alteration. We additionally consider particular interfaces to relevance feedback frameworks and qualities of searchers that can influence the utilization and achievement of relevance feedback frameworks.
Enhanced Web document retrieval using automatic query expansion
Journal of The American Society for Information Science and Technology, 2004
The ever growing popularity of the Internet as a source of information, coupled with the accompanying growth in the number of documents made available through the World Wide Web, is leading to an increasing demand for more efficient and accurate information retrieval tools. Numerous techniques have been proposed and tried for improving the effectiveness of searching the World Wide Web for documents relevant to a given topic of interest. The specification of appropriate keywords and phrases by the user is crucial for the successful execution of a query as measured by the relevance of documents retrieved. Lack of users' knowledge on the search topic and their changing information needs often make it difficult for them to find suitable keywords or phrases for a query. This results in searches that fail to cover all likely aspects of the topic of interest. We describe a scheme that attempts to remedy this situation by automatically expanding the user query through the analysis of initially retrieved documents. Experimental results to demonstrate the effectiveness of the query expansion scheme are presented.
Provision of Relevant Results on web search Based on Browsing History
Journal of Telematics and Informatics, 2014
Different users submit a query to a web search engine with different needs. The general type of search engines follows the "one size fits all" model which is not flexible to individual users resulting in too many answers for the query. In order to overcome this drawback, in this paper, we propose a framework for personalized web search which considers individual's interest introducing intelligence into the traditional web search and producing only relevant pages of user interest. This proposed method is simple and efficient which ensures quality suggestions as well as promises for effective and relevant information retrieval. The framework for personalized web search engine is based on user past browsing history. This context is then used to make the web search more personalized. The results are encouraging.
Enhancing the Search Result for User QueryUsing Iterative User Feedback
International Journal of Innovative Research in Computer and Communication Engineering, 2015
In real scenario when users submit a search query to a search engine, each user may have different search goals. We can improve search engine relevance by analyzing user search goals. We present an approach that infers user search goals by analyzing search engine query logs. Our approach discovers different user search goals for a query by clustering the proposed feedback sessions. Users information needs can be captured with the help of feedback sessions. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users on results. Secondly, we also generate pseudo-documents for better representation of the feedback sessions for clustering. Finally, we present Classified Average Precision (CAP) to evaluate the performance of inferring user search goals. For an ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we present a novel approach to Enhance Search Result For User Query Using Iterative User Feedback.
Query Recommendation for Improving Search Engine Results
recently, search engines become more critical for finding information over the World Wide Web where web content growing fast, the user's satisfaction of search engine results is decreased. This paper proposes a method for suggesting a list of queries that are related to the user input query. The related queries are based on previously issued queries by the users. The proposed method is based on clustering process in which groups of semantically similar queries are detected. This facility provides some queries which are related to the queries submitted by users in order direct them toward their required information. This method not only discovered the related queries but also rank them according to a similarity measure. Finally the method has been evaluated using real data sets from the search engine query log.
IJERT-A Survey on Incorporating User Behaviour to Enhance the Web Search
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/a-survey-on-incorporating-user-behaviour-to-enhance-the-web-search https://www.ijert.org/research/a-survey-on-incorporating-user-behaviour-to-enhance-the-web-search-IJERTV2IS121265.pdf Now-a-days millions of users search data on web daily. It has become an integrated part of everyone's life. Generally when we search data we have some intension to search. When we enter a query some links will be given by a search engine. But what about relevancy of those links to the user's actual need. Hence by incorporating user profile we can improve the searching process. By providing more satisfactory results to the user we will try to match results to the user's need. Many search engines are taking user profile into consideration, but some of them use user's interest hierarchy, many of them uses past history and so on. Re-ranking techniques are used to match links to the user's interest. Indexing and clustering is also used by many search engines to improve relevancy of the result.
Personalize Web Search Using User FeedbackSessions
International Journal of Innovative Research in Computer and Communication Engineering, 2014
In a web based application; different users may have dissimilar search goals when they submit it to a search engine. For a broad-topic and vague query it is difficult. Here we suggest a novel approach to infer user search goals by examining search engine inquiry logs. This is typically exposed in cases such as these: Dissimilar users have different upbringings and interests. However, real personalization cannot be attained without accurate user profiles. We propose aoutline that enables large-scale assessment of personalized search. The goalmouth of personalized IR (information retrieval) is to reappearance search results that better match the user intent. First, we propose aoutline to discover different user hunt goals for a query by clustering the future feedback sessions. Feedback sessions are getting built from user click-through woods and can efficiently reflect the info needs of users. Second, we propose an approach to make pseudo-documents to better signify the feedback meeti...
A Survey on Optimization of User Search Goals by Using Feedback Session
Data Mining refers to acquiring knowledge from large amounts of data. In the recent years the lots of surfing is done through web searching. When the information is retrieved the users clicks on particular URL, based n that click rate, ranking will be done automatically. This search engine significantly reduces the computation time required for partition of the dataset. It will also reduce the original dataset into simplified dataset. It also simplifies the data set and finds the relevant document based on users feedback. It also helps in reducing the iteration and improves the performance. Analyzing user search goal is needed to provide best result for which the user looks in the internet. Feedback sessions have been clustered to discover different user search goals for query. Pseudo-document is generated through feedback sessions for clustering. With this, the original search results are restructured. The performance of restructured search results is evaluated by classified average precision (CAP). This evaluation is used as feedback for selecting the optimal user search goals.