ICPCSEE 2017: Data Science pp 100-109 | Cite as

Desktop Data Driven Approach to Personalize Query Recommendation

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 727)

Abstract

Query recommendation is an effective method to help users describe their search intentions. In a personalized system, cold-start and the data sparsity were unavoidable, which directly lead to deficient performance of personalizing. As a significant part of a user’s personal information space, a personal computer owns lots of documents relevant to his or her interest. Therefore, desktop data was introduced to construct a user’s preference model. Furthermore, considering the variety of desktop data, relationship between search task and work task was simultaneously exploited to predict a user’s specific information need. Ten volunteers joined experiments to evaluate the potential of desktop data. A series of experiments were conducted and the results proved that desktop data greatly contributed to providing effective personalized reference words. Besides, the results demonstrated that a user’s long-term interest model performed steadier than work task context, but the most valuable words were the top-3 words extracted from the work context.

Keywords

Query recommendation Desktop data User model Work task Search task 
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Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61402220), Scientific Research Project of Education Bureau of Hunan Province, China (Grant No. 15C1186), the Construct Program for the Key Discipline in University of South China (No. NHxk02), the Construct Program for Innovative Research Team in University of South China.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyUniversity of South ChinaHengyangChina

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