Total views : 288

The Potential Knowledge Recommendation System using User’s Search Logs

Affiliations

  • Creative Information and Computer Institute, Korea University, Korea, Republic of

Abstract


Background/Objectives: This paper proposes a potential query recommendation system based on the user search history so that information search system users can express their potential information needs in a query, and the information they want can be searched. Methods/Statistical Analysis: The proposed system used users’ search query to analyze the associative relationship with existing users’ search history, and extracted users’ potential information needs. The extracted potential information needs are recommended to users in the recommendation query. Findings: This paper used 27,656 pieces of search history data for analyzing the utility of the proposed system and conducted a behavioral experiment. The experiment found that the subjects showed a statistically higher level of satisfaction when using the proposed system than when using a general search engine. Improvements/Applications: In the future, it will be possible to secure the reliability of recommended queries by expanding and solidifying the search history through researches on personalization.

Keywords

Information Retrieval, Potential Knowledge, Query, Recommendation, Search Log.

Full Text:

 |  (PDF views: 313)

References


  • Kinam Park, Hyesung Jee, Taemin Lee, Soonyoung Jung, Heuiseok Lim, Automatic extraction of user’s search intention from web search logs. Multimedia Tools and Applications. 2012 November; 61(1):145-62.
  • Young-an Kim, Gun-Woo Park, An Efficient Extended Query Suggestion System Using the Analysis of Users’ Query Patterns. Journal of the Korean Institute of Communication Sciences. 2012 July; 37(7):619-26.
  • Ji-Hye Kim, Doo-Soon Park. Development of the Goods Recommendation System using Association Rules and Collaborating Filtering. The Journal of Korean Association of Computer Education. 2005 August; 9(1):71-80.
  • Schuemie MJ, Kang N, Hekkelman ML, Kors JA. GeneE: Gene and protein query expansion with disambiguation. Bioinformatics. 2010 January; 26(1):147-48.
  • Xu J, Croft WB. Query Expansion Using Local and Global Document Analysis. Proceeding of the 19th international ACM SIGIR, Switzerland; 1996. p. 4-11.
  • Andreas Harth, Aidan Hogan, Jürgen Umbrich, Stefan Decker. Building a Semantic Web Search Engine: Challenges and Solutions. Proceedings of the 3rd XTech, lreland; 2008.
  • Ashwin Kumaar M, Palani Thanaraj. Feature Extraction of Arterio-Venous Malformation Images using Grey Level Co-Occurrence Matrix. Indian Journal of Science and Technology, 2015; 8(35):1-5.
  • GunWoo Park, JinGi Chae, Dae Hee Lee, SangHoon Lee. User Intention based Personalized Search: HPS (Hierarchical Phrase Serch), Proceedings of the WSEAS, USA; 2008. p. 266-76.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.