Abstract
Aimed to the inherent detects in present information retrieval service, this paper proposed an approach to exploit desktop context to provide personalized recommendation service. The files restored on the local disk and the documents opened in a work scenario were regarded as two separate parts serving for personalizing. The algorithm for extracting to desktop resources to build the long-term document model was introduced in detail, which further provides information to build a user’s interest model. And the method to establish the short-term model in a work scenario to predict the user’s current information need was also introduced. The experiments were conducted to offer recommended items in a message window and analyzed the implicit information of each user’s corresponding behaviors. The results showed that users were interested in recommended items and the performance was stable.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jian, W., Yi, Z.: Utilizing Marginal Net Utility for Recommendation in E-commerce. In: Proc. 34th SIGIR, Beijing, China (2011)
Bhogal, J., Macfarlane, A., Smith, P.: A review of ontology based query expansion. Information Processing and Management 43(4), 866–886 (2007)
Kotov, A., Bennett, P.N., White, R.W., et al.: Modeling and Analysisi of Cross-Session Search Tasks. In: Proc. 34th SIGIR, Beijing, China (2011)
Zhu, Z.A., Chen, W., Minka, T., et al.: A novel click model and its applications to online advertising. In: Proc. 3rd WSDM, New York, USA, pp. 321–330 (2010)
Li, X., Yu, Y.: Research on Self-adaptive Recommendation System Based on Implicit Feedback. Computer Engineering 36(16), 270–272 (2010)
White, R.W., Kelly, D.: A Study on the Effects of Personalization and Task Information on Implicit Feedback Performance. In: CIKM, Arlington, Virginia, USA, pp. 297–306 (2006)
Guo, F., Liu, C., Kannan, A., et al.: Click Chain Model in Web Search. In: Proc. 18th WWW, Madrid, Spain, pp. 11–20 (2009)
Chapelle, O., Zhang, Y.: A Dynamic Bayesian Network Click Model for Web Search Ranking. In: Proc. 18th WWW, Madrid, Spain, pp. 1–10 (2009)
Teevan, J., Dumais, S.T., Horvitz, E.: Potential for Personalization. Proc. ACM Transactions on Computer-Human Interaction special issue on Data Mining for Understanding User Needs 17(1), 1–31 (2010)
Chirita, P.-A., Firan, C.S., Nejdl, W.: Summarizing local context to personalize global web search. In: Proc. 15th CIKM, Arlington, Virginia, USA (2006)
Teevan, J., Morris, M.R., Bush, S.: Discovering and using groups to improve personalized search. In: Proc. 2nd WSDM, Barcelona, Spain (2009)
Peery, C., Wang, W., Marian, A., Nguyen, T.D.: Multi-dimensional search for personal information management systems. In: Proc. 11th International Conference on Extending Database Technology: Advances in Database Technology, Nantes, France (2008)
Li, Y., Meng, X.: Supporting context-based query in personal DataSpace. In: Proc. 18th CIKM, Hong Kong, China, pp. 1437–1440 (2009)
Aji, A., Wang, Y., Agichtein, E., Gabrilovich, E.: Using the Past to Score the Present: Extending Term Weighting Models Through Revision History Analysis. In: Proc. CIKM, Toronto, Ontario, Canada (2010)
Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing Search via Automated Analysis of Interests and Activities. In: Proc. 28th ACM Conference on Research and Development in Information Retrieval, Salvador, Brazil (2005)
Freund, L.S.: Exploiting task-document relations in support of information retrieval in the workplace. Doctoral dissertation. Faculty of Information Studies, University of Toronto (2008)
Yang, X.-H., Jiang, H., Ma, J.-Y.: Query Expansion Based on Task Context. In: Proc. 8th National Symposium of Search Engine and Web Mining, Chengdu, China (2010)
White, R.W., Bailey, P., Chen, L.: Predicting User Interests from Contextual. In: Proc. 32nd SIGIR, Boston, Massachusetts, USA (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Li, Xy. et al. (2012). Personalized Recommendation Based on Desktop Context. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25781-0_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-25781-0_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25780-3
Online ISBN: 978-3-642-25781-0
eBook Packages: EngineeringEngineering (R0)