Search engine is one of the most important applications in today's internet. Users collect required information through the search engine in the internet. Analyzing user search goals are essential to provide best result for which the user looks for in the internet. In existing system, various techniques such as Feedback session, goal text, Pseudo-documents restructuring search result based on term frequency are used to infer user search goals. Existing search results based on term frequency (keywords) which may display unwanted results. In proposed system "Classified Average Precision (CAP)" algorithm is used to understand user search goals efficiently and evaluate the performance of inferring user search goals. Phrase search is performed in proposed system instead of keyword search. Initially Noun Phrase of user query is framed using natural language processing. Framed noun phrases are searched in webpages available in Internet. Term frequency of each noun phrase is found in Pseudo document i.e., finding number of webpages a particular noun phrase is occurred. Based on term frequency, place the webpage/document which contain only the above noun phrases at top link. Here user needs is highlighted and provides a user friendly search engine. Performance of inferring user search goal is evaluated using a new CAP algorithm.

Nowadays, a rapid development in the communication technology and increasing the usability of powerful portable devices, mobile users can use their mobile devices to access the information. One of the active areas is the mining and prediction of users' mobile commerce behaviors such as their movements and purchase transactions. The important issue is to mine the rare frequent items from database to satisfy the user needs. In this paper, we propose a technique that can efficiently satisfy the user needs. It predicts the frequent item based on the user selection. Systolic tree implementation is used to predict the frequently moved item in the database. The main aim is to recommend the stores and items previously to unknown user. We evaluate our system in real world and deliver good performance in terms efficiency and scalability.