Authors A.V. Smirnov, N.G. Shilov
Month, Year 05, 2011 @en
Index UDC 004.8
Abstract Group recommendation systems recommend some solutions (related to products, technologies, tools, material and business models) based on user group requirements, preferences and willingness to compromise and to be pro-active. The paper proposes an approach to developing a group recommendation system for collaborative product development based on such technologies as user and group profiling, context management, decision mining. The system allows accumulation of knowledge about user actions and decisions and uses self-organization mechanisms to compromise between group and individual preferences. Proposed approach enables formulation of recommendations for users of the same group anticipating their possible further actions and decisions.

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Keywords Collaborative recommendation system; group profiling; context management.
References 1. McCarthy K., Salamo M., Coyole L., McGinty L., Smyth B., Nixon P. Group Recommender Systems: A Critiquing Based Approach, In IUI '06: Proceedings of the 11th international conference on Intelligent user interfaces, 2006. – P. 267-269.
2. Garcia I., Sebastia, L., Onaindia, E., Guzman C.A Group Recommender System for Tourist Activities, In EC-Web 2009: Proceedings of E-Commerce and Web Technologies // The 10th International Conference (2009), Springer, LNCS 5692, 2009. – P. 26-37.
3. Moon S.K., Simpson T.W., Kumara S.R.T. An agent-based recommender system for developing customized families of products // Journal of Intelligent Manufacturing, Springer. – 2009. – Vol. 20, № 6. – P. 649-659.
4. Chen Y.-J., Chen Y.-M., Wu M.-S. An expert recommendation system for product empirical knowledge consultation, In ICCSIT2010 // The 3rd IEEE International Conference on Computer Science and Information Technology, IEEE. – 2010. – P. 23-27.
5. Baatarjav E.-A., Phithakkitnukoon S., Dantu, R. Group Recommendation System for Facebook, In OTM 2008: Proceedings of On the Move to Meaningful Internet Systems Workshop (2008), Springer, LNCS 5333, 2009. – P. 211-219.
6. Romesburg H.C. Cluster Analysis for Researchers, Lulu Press, California, 2004.
7. Flake G.W., Lawrence, S., Giles, C.L., Coetzee, F. Self-Organization and identification of Web Communities // IEEE Computer. – 2002. – Vol. 35, №. 3. – P. 66-71.
8. Smirnov A., Levashova T., Kashevnik A., Shilov N. Profile-based self-organization for PLM: approach and technological framework In PLM 2009: Proceedings of the 6th International Conferecne on Product Lifecycle Management, 2009, Electronic proceedings.
9. Smirnov A., Pashkin M., Chilov N. Personalized Customer Service Management for Networked Enterprises, In ICE 2005: Proceedings of the 11th International Conference on Concurrent Enterprising, 2005. – Р. 295-302.
10. Smirnov A., Pashkin M., Levashova T., Kashevnik A., Shilov N. Context-Driven Decision Mining, Encyclopedia of Data Warehousing and Mining. Hershey (Ed. by J. Wang), New York, Information Science Preference, Second Edition. – 2008. – Vol. 1. – P. 320-327.
11. Rozinat A., van der Aalst W.M.P. Decision Mining in Business Processes, BPM Center Report no. BPM-06-10, 2006.
12. Левашова Т.В. Пашкин М.П., Шилов Н.Г. Онтолого-ориентированный многоагентный подход к построению систем интеграции знаний из распределённых источников // Информационные технологии и вычислительные системы. – 2002. – № 1. – C. 62-82.
13. Нефедов В.Н., Осипова В.А. Курс Дискретной математики. – М.: МАИ, 1992. – 262 с.
14. Кук Д., Бейз Г. Компьютерная математика. – М.: Наука, 1990. – 383 c.
15. Tan P.-N., Steinbach M., Kumar V. Introduction to Data Mining, Addison Wesley, 2005. – 769 p.

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