Query Recommendation Based on User Browsing History

The most commercial search engines return the search list by matching the user query terms with the documents available in its database. The relative effectiveness of search list is highly affected by the extent to which the query keywords map to the actual need of user. User generally forms the short, ambiguous and instant queries which lead to inclusion of irrelevant documents in the search list. One well known solution to this problem is query suggestion also known as query recommendation. For query recommendations, the search systems maintain the query logs at server sites to better understand user’s information need. But till now, the current search systems have partially solved this problem as they roughly offer the similar queries to all the users regardless of their actual interests. In this paper, A novel query recommendation technique based on user browsing patterns is proposed where user interest factor in different domains are computed and used to recommend personalised queries to each individual. The experimental evaluation shows that system is able to assist user in query formation phase and efficiently reduces the search space and time required to get the desired information.