Search Engine Based on Mobile User Customization

As the amount of Web information grows rapidly; Search engines must be able to retrieve information according to the user's preference. In this paper, we propose Ontology Based Personalized Mobile Search Engine (OBPMSE) that captures user's interest and preferences in the form of concepts by mining search results and their clickthroughs. OBPMSE profile the user's interest and personalized the search results according to user's profile. OBPMSE classifies these concepts into content concepts and location concepts. In addition, user's locations (positioned by GPS) are used to supplement the location concepts in OBPMSE. The user preferences are organized in an ontology-based, multifacet user profile, used to adapt a personalized ranking function which in turn used for rank adaptation of future search results. We propose to define personalization effectiveness based on the entropies and use it to balance the weights between the content and location facets. In our design, the client collects and stores locally the clickthrough data to protect privacy, whereas heavy tasks such as concept extraction, training, and reranking are performed at the OBPMSE server. OBPMSE provide client-server architecture and distribute the task to each individual component to decrease the complexity.