Learning to Rank Search Result for Time Sensitive Queries

In recent years, semantic search for relevant documents on web has been an important topic of research. The World Wide Web consists of millions of interconnected web pages that provide information to the users. The WWW is expanding and growing in size and the complexity of the web pages. That’s why it is necessary to retrieve the best or the web pages that are more relevant in terms of information for the query entered by the users. Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. The search is generally carried out based on the similarity of the documents being searched for. , and then “boosts� the scores of the most recent documents, to privilege recent documents over older ones. Unfortunately, topic similarity alone is not always sufficient for document ranking. Earlier work was based on improving retrieval for “recency� queries that target recent documents. Retrieval of temporal queries can be improved by considering the time dimension implicitly or explicitly.