Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related ork
  2.1 Query Log
  2.2 BB’s Algorithm
 3. Proposed Work
  3.1 User interface
  3.2 Profile Generation Module
  3.3 Query Clustering Module
  3.4 Query Recommendation Module
 4. Result Analysis
  4.1 Generation of query cluster based on Sim combined
  4.2. Personalised query generation
 5. Conclusion and Future Scope
 References

키워드

저자

• Shilpa Sethi [ Department of Computer Science, YMCA University of Science and Technology, Faridabad ]
• Ashutosh Dixit [ Department of Computer Science, YMCA University of Science and Technology, Faridabad ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605