20. A method of generating suggestions for search, the method comprising: using a computer processor and storage, mining user query activity from user activity history logs of user sessions on an e-commerce site; analyzing, and building a probability graph from a user query activity history log of user queries that have been executed by users of the e-commerce site during the user query activity which includes at least one of searches, Buy It Now actions, bids, ask seller a question, watches, views, and offers, wherein at least some queries are seen in the user query activity history log as a node on the graph, edges connecting the at least some queries are based on follow counts of queries in user sessions, and popularity of queries and user behavior in terms of engagement are properties seen as edges of the graph; and pruning the graph, based on edge properties to obtain the best neighbors for every query which improve popularity of queries and user behavior in terms of engagement.
20. A method of generating suggestions for search, the method comprising: using a computer processor and storage, mining user query activity from user activity history logs of user sessions on an e-commerce site; analyzing, and building a probability graph from a user query activity history log of user queries that have been executed by users of the e-commerce site during the user query activity which includes at least one of searches, Buy It Now actions, bids, ask seller a question, watches, views, and offers, wherein at least some queries are seen in the user query activity history log as a node on the graph, edges connecting the at least some queries are based on follow counts of queries in user sessions, and popularity of queries and user behavior in terms of engagement are properties seen as edges of the graph; and pruning the graph, based on edge properties to obtain the best neighbors for every query which improve popularity of queries and user behavior in terms of engagement. 22. The method of claim 20 , the method employing noise removal techniques including one of historical inventory count, current inventory count, and a stemming algorithm.