Intention mining

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In data mining, intention mining or intent mining is the problem of determining a user's intention from logs of their behavior in interaction with a computer system, such as a search engine.

Some authors model the intentions as an intentional process model in order to have a better understanding of the human way of thinking.[1]

Application

Intention Mining has already been used in several domains:

  • Web search : (Hashemi et al., 2008),[2] (Zheng et al., 2002),[3] (Strohmaier & Kröll, 2012),[4] (Kröll & Strohmaier, 2012),[5] (Park et al., 2010),[6] (Jethava et al., 2011),[7] (González-Caro & Baeza-Yates, 2011),[8] (Baeza-Yates et al., 2006) [9]
  • Business : Workarounds,[10] (Epure, 2013) [11]
  • Engineering : Entity Relationship modelling,[12] Method Engineering,[13] (Laflaquière et al., 2006),[14] (Clauzel et al., 2009) [15]
  • Home video : (Mei et al., 2005) [16]

See also

References

  1. Supervised intentional process models discovery using Hidden Markov models, Khodabandelou, G. ; Hug, C. ; Deneckere, R. ; Salinesi, C. IEEE Seventh International Conference on Research Challenges in Information Science (RCIS), 2013
  2. Hashemi, R.R., Bahrami, A., LaPlant, J. & Thurber, K. (2008). Discovery of Intent through the Analysis of Visited Sites. In Arabnia, H.A & Hashemi, R.R., (Eds.), Proceedings of the 2008 International Conference on Information & Knowledge Engineering (pp. 417-422). CSREA Press.
  3. Zheng, C., Fan, L., Huan, L., Yin, L., Wei-Ying, M. & Liu, W. (2002, November). User Intention Modeling in Web Applications Using Data Mining. World Wide Web, 5 (3) 181-191.
  4. Strohmaier, M. & Kröll, M. (2012). Acquiring knowledge about human goals from Search Query Logs. Information Processing & Management, 48 (1) 63-82.
  5. Kröll, M. & Strohmaier, M. (2009). Analyzing Human Intentions in Natural Language Text. In Gil, Y., & Fridman Noy, N. (Eds.), Proceedings of the 5th International Conference on Knowledge Capture (pp. 197-198). New York, NY, USA: ACM.
  6. Park, K., Lee, T., Jung, S., Lim, H. & Nam, S. (2010). Extracting Search Intentions from Web Search Logs. In 2nd International Conference on Information Technology Convergence and Services (pp. 1-6).
  7. Jethava, V., Calderón-Benavides, L., Baeza-Yates, R., Bhattacharyya, C. & Dubhashi, D. (2011). Scalable Multi-Dimensional User Intent Identification using Tree Structured Distributions. In Ma, W.-Y., Nie, J.-Y., Baeza-Yates, R.A., Chua, T.-S. & Croft, W.B. (Eds.), Proceedings of the 34th International ACM Conference on Research and development in Information Retrieval (pp. 395-404). New York, NY, USA: ACM.
  8. González-Caro, C. & Baeza-Yates, R. (2011). A multi-faceted approach to query intent classification. In Grossi, R., Sebastiani, F. & Silvestri F. (Eds.), Proceedings of the 18th International Conference on String Processing and Information Retrieval (pp. 368-379). Berlin, Heidelberg: Springer.
  9. Baeza-Yates, R., Calderón-Benavides, R. & González-Caro, C. (2006). The intention behind web queries. In Crestani, F., Ferragina, P. & Sanderson, M. (Eds.), Proceedings of the 13th International Conference on String Processing and Information Retrieval (pp. 98-109). Berlin, Heidelberg: Springer.
  10. Outmazgin, N. & Soffer, P. (2010). Business Process Workarounds: What Can and Cannot Be Detected by Process Mining. Lecture Notes in Business Information Processing, 147, 48-62.
  11. Epure, E.V. (2013). Intention-mining: A solution to process participant support in process aware information systems (Master thesis). Utrecht University, The Netherlands.
  12. Supervised intentional process models discovery using Hidden Markov models, Khodabandelou, G. ; Hug, C. ; Deneckere, R. ; Salinesi, C. IEEE Seventh International Conference on Research Challenges in Information Science (RCIS), 2013
  13. Intelligent Agile Method Framework, Jankovic M., Bajec M., Khodabandelou G., Deneckere R., Hug C., Salinesi C., 8th International Conference on Evaluation of Novel Approaches to Software Engineering 2013
  14. Laflaquière, J., Lotfi, Settouti, S., Prié, Y. & Mille, A. (2006). Trace-Based framework for experience management and engineering. In Gabrys, B, Howlett, R.J. & Jain, L.C. (Eds.), Proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 1 (1) Berlin, Heidelberg: Springer, 1171-1178.
  15. Clauzel, D., Sehaba, K., & Prié, Y. (2009). Modelling and Visualising Traces for Reflexivity in Synchronous Collaborative Systems. In Badr, Y., Caballé, S., Xhafa, F., Abraham, A., & Gros, B. (Eds.), Proceedings of the 1st International Conference on Intelligent Networking and Collaborative Systems (pp. 16-23). IEEE.
  16. Mei, T., Hua, X.-S. & Zhou, H.-Q. (2005). Tracking users' capture intention: a novel complementary view for home video content analysis. In Proceedings of the 13th annual ACM International Conference on Multimedia (pp. 531-534). New York, NY, USA: ACM.
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