Enhancement in web service provision using combination of collaborative technique and association rule generation

Search engine provide query recommendation which suggest user to choose any one query from their recommendation list according to their necessities. Such systems had been expected to have a good result, especially in E-commerce and E-learning environments. However, although they have been intensively explored in the Web Mining and Machine learning fields, and there have been some commercialized systems, the quality of the recommendation and the user satisfaction of such systems are still not conclusive, since the search result provided by the search engine will not be a satisfactory one in most of the cases. In this paper we proposed a robust model that leverages most appropriate and promising search result for user queries. The system uses useful information on user behavior and their activities when searching online and the content they are interested in viewing for the result which they obtained in response to their query by search engine. The proposed technique able to provide required promising result as a response rather than returning the result which has maximum hits for their search queries. Keyword: E-commerce, E-Learning, Recommendation system, Search engine Introduction: Web search is an area of the information technology industry where artificial intelligence and particularly knowledge engineering techniques can be applied with potentially significant impacts. Currently users face a still increasing amount of data for their queries, since the search engine provide the result which has maximum hit rate. Such result cannot able to satisfy the user as their interested content may not present in those results. It is quite reasonable assumption that a series of queries characterize the effort of what the user want to find better than the single query. Therefore the search engines were designed in such a way to guide the user for selecting queries which results in the appropriate thing they need. Recently, some of the major search engines have introduced a new “Search History” feature, which allows users to track their online search by recording their queries and clicks. This history includes a sequence of the queries displayed in reverse chronological order together with their corresponding clicks. The proposed system which organize user search histories and learning of user behavioral patterns not for providing relevant query pattern but for getting the result in which the user are already interested to know, by the way it could identify the hidden data which is