Optimized Mobile Search Engine

We propose a Optimized Personal Search Engine for mobile , that takes the users’ preferences and data in the form of concepts by analyzing their click through data. I give the importance of location information in mobile search, so my Optimized Personal Search Engine classifies these concepts like content concepts and location concepts. In addition, users locations (positioned by GPS) are used to give the location concepts in Optimized Personal Search Engine . The user preferences are organized in an ontology-based and the multi-facet user profile is used to adapt a personalized ranking function for rank adaptation of future search results. To characterize the concepts associated with a query and their relevance’s to the users need and four entropies are introduced to balance the weights between the content and location facets. Based on the client-server model, i also present a detailed architecture and design for implementation of Optimized Personal Search Engine. In our design, the client collects and stores locally the click through data and to protect privacy such as heavy tasks such as concept extraction, training and reran king are performed at the Optimized Personal Search Engine server. Moreover, I address the privacy issue by restricting the information in the user profile exposed to the Optimized Personal Search Engine server with two privacy parameters. We prototype Optimized Personal Search Engine on the Google Android platform. Experimental results show that Optimized Personal Search Engine significantly improves the precision comparing to the baseline. Keyterms:Optimized mobile Search Engine, mobile, ClientServer Model, location and content based 1.INTRODUCTION ABOUT MOBILE SEARCH:The major and main problem in mobile search is that the interactions between the users and search engines are limited by the small form factors of the mobile devices. For that result, mobile users tend to submit shorter, and hence, there are more ambiguous queries compared to their web search counter parts. So in order to return highly relevant results to the users, the mobile search engines must be able to profile the users’ interests and personalize the search results according to the users’ profiles. One of the practical approach to capturing a user’s interests for personalization is to analyze the user’s Click through data. The researcher Leung developed a search engine personalization method based on users’ concept preferences and showed that it is more effective and most of the previous work assumed that all concepts are of the same type. we Observing the need for different types of concepts and we present in this paper a Optimized Personal Search Engine, that represents different types of concepts in different ontology’s. we recognizing the importance of location information in mobile search, we are separate concepts into location concepts and content concepts. A major problem in mobile search is that the interactions between the users and search engines are limited by the small form factors of the mobile devices. So as a result, the mobile users tend to submit shorter, and hence, there are more ambiguous queries compared to their web search counterparts. So In order to return highly relevant results to the users, the mobile search engines must be able to profile the users’ interests and personalize the search results according to the users’ profiles. The practical approach to capturing a user’s interests for personalization is to analyze the user’s click through data. The Leung et al. was developed a search engine personalization method based on users’ concept preferences and showed that it is more effective than methods that are based on page preferences. So most of the previous work assumed that all concepts are of the same type and Observing the need for different types of concepts, So we present in this paper a Optimized Personal Search Engine which represents different types of concepts in different ontology’s. In particular, we recognizing the importance of location information in mobile search, we just separate concepts into location concepts and content concepts like that. For example, the user who is planning to visit Japan may issue the query “hotel,” and then click on the search results about hotels in Japan. So From the click through of the query “hotel,” Optimized Personal Search Engine can learn the user’s content preference ( “room rate” and “facilities”) and location preferences (“Japan”). Accordingly, the Optimized Personal Search Engine will favor results that are concerned with hotel information in Japan for future queries on “hotel.” The introduction of location preferences offers Optimized Personal Search Engine an additional dimension for capturing a user’s interest and an opportunity to enhance search quality for users. So to incorporate context information revealed by user mobility, and we also take into account the visited physical locations of users in the Optimized Personal Search Engine. And since this information can be conveniently obtained by GPS devices, and it is hence referred to as GPS locations. The GPS locations play an important role in mobile web search. For example, let if the user, and who is searching for hotel information, that is currently located in “Shinjuku, Japan,” and his/her position can be used to personalize the search results to favor information about nearby hotels. There, we can see that the GPS locations (i.e., “Shinjuku, Tokyo”) help reinforcing the user’s location preferences (i.e., “Japan”) derived from a user’s search activities to provide the most relevant results. And our proposed framework is capable of combining a user’s E.Chaitanya et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5553-5559