Abstract
Personalized search is an essential research area that has main goal to determine the uncertainty of query terms. In order to enhance the relevance of search results, personalized search engines form user profiles which capture the users' personal preferences and by using those preferences find out the actual goal of the input query. By using User profile we can rank the documents in a search engine according to the query which is submitted by user. A better user profiling strategy is an important and primary component in search engine personalization. In this work, we propose a scheme that supports mining a user's conceptual preferences from users' click through data resulted from web search. This discovered preference is helpful to adapt a search engine's ranking function. In the system, an absolute set of conceptual preferences is derived for a user such as the concepts extracted from the search results and the click through data. After that, a Concept-based User Profile (CUP) in other words a concept ontology tree is generated. Our system proposes a novel approach such as Link-Click-Concept based Ranking Algorithm. The proposed system considers the concept for the user profile construction and has high efficiency than the existing system.
Highlights
At the present time the amount of information on the web is increasing rapidly, which has become increasingly harder for the web search engines to get the information that satisfies the userβs individual interests
To discover further the benefits of concept ontology, here we propose a innovative Concept-based User Profiling (CUP) technique which is used to capture usersβ topical preferences
Search engines performance can be improved by an Βaccurate user profiles
Summary
At the present time the amount of information on the web is increasing rapidly, which has become increasingly harder for the web search engines to get the information that satisfies the userβs individual interests. The queries submitted by the user have lower length. This may lead to uncertainty for the search engine to recover the results for a particular user query. Foremost commercial search engines offers query suggestions which is used for users to make more valuable queries and it is used to improve userβs search experience. Whenever a user submitted a query, the meaningful related terms for a given query are offered to help the user recognize terms that they really want. It will improve the efficiency of retrieval. Yahooβs β Tryβ and Googleβs βSearches related toβ features offers correlated queries for narrowing search
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