Online Submission!

Open Journal Systems

A Comparative Study of Context-Based Information Refinding

A.P. Nivethith, D. KeranaHanirex, Dr.K.P. Kaliyamurthie

Abstract


We present a comparative study of query analysis for efficient context-based information refinding system. Refinding what we have done before is a common behavior of human in real life. According to the human natural recall characteristics, users allow to refinding web pages which have seen before. Psychological studies show that context under which information was accessed can helps as a powerful cue for information recall. Here context including time, place and concurrent activity could serves as a useful information recall clues. We build a recall based query model, linking to the previous accessedinformation contents. We explore the use of context cluster and association to efficiently process the context-based refinding queries. In context memory dynamically evolve in life cycles; there are memory’s decay and reinforcement phenomena. Thus take contextual information refinding of various paper and make comparison of how to contextual cues to refind the WebPages effectively.

Full Text:

PDF

References


GoogleWeb History, http://www.google.com/history,

Tangjian Deng, Liang Zhao, Hao Wang, Qingwei

Liu, and Ling Feng, Senior Member, I EEE “ ReFinder:

A Context-Based Information Refinding System”

S. Won, J. Jin, and J. Hong, “ Conte xtual Web

History: Using Visual and Contextual Cues to Improve

Web Browser History,” Proc.SIGCHI Conf. Human

Factors in Computing Systems (CHI), 2009.

B. MacKay, M. Kellar, and C. Watters, “An

Evaluation of Landmarks for Re-Finding Informat ion

on the Web,” Proc. Extended Abstracts on Human

Factors in Co mputing Systems (CHI ’05 EA ), 2005.

J. Hailpern, N. Jitkoff, A. Warr, R. Karahalios, K.

Sesek, andN. Sh krob, “ YouPivot: Improving Recall

with Contextual Search,” Proc. SIGCHI Conf. Human

Factors in Computing Systems (CHI), 2011.

E. Adar, J. Teevan, and S.T. Du ma is, “ Large Scale

Analysis of Web Revisitation Patterns,” Proc. SIGCHI

Conf. Human Factors in Computing Systems (CHI),

R. Capra, M. Pinney, and M.A. Perez-Quinones,

“Refinding Is Not Finding Again,” technical report,

Aug. 2005.

Y. Chen and G. Jones, “Integrating Memory Context

into Personal Informat ion Re-Finding,” Proc. Second

Symp. Future Directions in Information Access, 2008

J. Teevan, “The Re :Sea rch Engine: Simultaneous

Support for Finding and Re-Finding,” Proc. 20th Ann.

ACM Symp. User Interface Software and Technology

(UIST), 2007.

S.K. Ty ler and J. Teevan, “ Large Scale Query Log

Analysis of Re-Finding,” Proc. Third ACM Int’l Conf.

Web Search and Data Mining(WSDM), 2010.

A.P.Nivethitha“efficiently conte xt-based

informat ion re-finding and page ranking”International

conference on electrical, communication and

computing,2014.




DOI: http://dx.doi.org/10.6084/ijact.v3i4.598

Refbacks

  • There are currently no refbacks.