Murtuza Kutub

Bio: Murtuza Kutub is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Spamdexing & Search analytics. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

User Web Search Behaviour

Abstract: Since the inception of the web searching technology, people have been searching for almost everything and anything on the internet. The ever-increasing dependency of users on these search engines and the dynamic nature of the World Wide Web has reduced the accuracy of the search results and increased the search time of an individual. Today, more than ever before, there is a need for search engines to be relevant and precise to the user’s needs and to be able to make decisions about what the user wants to search, and should be able to suggest him similar or related topics of his interest. This increasing need of the search engine to become a decision engine [1] (term coined by Stefan Weitz [2]) brought to fore various creative technological ideas like Tag clouds and AutoComplete [3]. For a better and more relevant search experience, it is crucial that we study the present search behavior of users and its corresponding response by the search engine. This work contemplates the nature of searches made and how they evolve from time to time. In this paper we examine and construe data from various angles and then provide our suggestions and conclusions for a better, more personalized and relevant search.

Personalized Query Based Search Techniques Using Association Rules

Abstract: In mobile based search major problem is that interaction between the user and search are controlled by little numeral of factors in the mobile plans. By observing of necessitate for dissimilar types of concepts, present personalized mobile search engine (PMSE), it capture the user preferences concepts by mining click through data. In PMSE the user preferences are ordered in an ontology-based, user profile to adapt a personalized ranking function for future search results. In proposed system introduce an association rule mining algorithm to collect the travel related query patterns and travel patterns from the original personal mobile search engine profile. Association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. They introduced association rules for discovering regularities between normal patterns and query related patterns in the personalized mobile search engine result.

Web Usage Classification and Clustering Approach for Web Search Personalization

Abstract: The increases in the information resources on the World Wide Web in search of the necessary information, as users navigate the Web with multiple sites. When user surfing the web which is a huge and complicated often miss their required searching pages. Web personalization is based on the Web usage logs of user's makes advantage of the knowledge required for the analysis of the content and structure of web sites promising to solve this problem by supporting one of the procedures. The search engine can affect the effectiveness of existing approaches, depending on the user profile, which is building more and more on the web pages or documents. In this paper, we propose an efficient and novel web search based on the individual classification and clustering method. The proposed approach classified the cluster data using frequent pattern mining and multilevel association rules for recurring relationship and cluster the web usage using Hierarchical methods with the navigating site and user interest for personalization. This approach process in advance to support the real time personalization and minimizes the cost reduction of preparation personalization resource in real time. The proposed approach is an effective personalization to the user's interest; in experimental research it has shown high precision measures.