How do users describe their information need: Query recommendation based on snippet click model

Incorporating Synonyms into Snippet Based Query Recommendation System

“…The key idea of that system follows that if user click certain result from list then it shows that the user has read that particular snippet and interested in that snippet and not in the result. But this system does not consider the location and synonyms also, which may be useful for improving the results of search [1].…”

“…Thus the query recommendation technique is proposed to present users with a list of possible choices whose information needs are relatively clear to search engines. By this means, users can exactly state their information need by clicking recommendation query links instead of inputting new queries [1].…”

“…Thus query recommendation function helps user to recognize their short formed and possibly ambiguous queries and return appropriate result to satisfy the user information need [1].…”

Adaptive information retrieval system based on fuzzy profiling

“…Recently, [18] integrated the content-based (TF-IDF) and the connectivity-based ranking algorithms using the clickthrough data to improve the search result for a web page. Another approach was proposed to develop a snippetbased algorithm to estimate the document relevance.…”

Query Click and Text Similarity Graph for Query Suggestions

“…In [13][14][15][16][17][18] authors have used snippet information of clicked URLs or search results returned from a query for query recommendation in different ways. These methods are not general and the extensibility is very low.…”