An Ontology Based Recommender System to Mitigate the Cold Start Problem in Personalized Web Search

With the increase in the diversity of data available on the web, excellence of various searches and the need for personalizing the search results arises. The densely distributed web and heterogeneous information environment creates challenges for search engines such as Storage space, crawling speed, computational speed and retrieval of most relevant documents. It becomes difficult to identify the relevancy of the result due to instability in the search query context. In this paper, the framework to personalize web search through modeling user profile by content based analysis and recommendation model is proposed. The framework will use knowledgebase in form of query hierarchy which is specified for individual user to filter discovered results. The proposed approach is also used to discover current search context of particular user by alluding useful links through item-item collaborative filtering techniques. Due to integration of content based analysis and item to item collaborative filtering algorithm, the proposed framework will retrieved the results of user context on query and also suggest links that had been already clicked by the users within same context.