Inferring user search goals with feedback sessions using clicked documents for related search recommendation

Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware they do not take into account the immediately preceding queries as context in query suggestion. Hence, the input queries are normally short and ambiguous. Query recommendation is a method to recommend web queries that are related to the user initial query which helps them to locate their required information more precisely. It also helps the search engine to return appropriate answers and meet their needs. Usually users have ambiguous keywords in their mind to represent their information need. Hence, it is not a good idea to generate relation between user query keywords for recommendations. In this paper, we have presented Related Search Recommendation (RSR) framework, which discovers keywords which are present in snippets clicked and unclicked documents in feedback session. Pseudo documents are generated from feedback sessions which reflect what users wish to retrieve.