15-05-2014, 02:13 PM
Problems and Solutions of Web Search Engines-IR-Tree An Efficient Index for Geographic Document Search
Abstract:
In internet a wide range of web information increases rapidly, user could wants to retrieve the information based upon their preference using search engines. Our paper going to propose a new type of search engine for web personalization approach. It will capture the interests and preferences of the user in the form of concepts of mining search results and their clickthroughs.
Our approach is to improve the search accuracy by means separating the concepts into content concepts and location concepts due to the important role location information plays in mobile search. Then organize them into ontologism to create an ontology-based, multi-facet (OMF).
Moreover, recognizing the fact that different users and queries may have different emphases on content and location information, we introduce the notion of content and location entropies to measure the amount of content and location information associated with a query, and click content and location entropies to measure how much the user is interested in the content and location information in the results.
As a result, we propose to define personalization effectiveness based on the entropies and use it to balance the weights between the content and location facets. Finally, based on the derived ontologism and personalization effectiveness; we train an SVM to adapt a personalized ranking function for re-ranking of future search.
We perform extensive experiments to compare the precision produced by our OMF profiles and that of a baseline method. Experimental results show that OMF improves the precision significantly compared to the baseline.