ISSN 2071-8594

Russian academy of sciences

Editor-in-Chief

Gennady Osipov

A.V. Smirnov, N.G. Shilov, A.V. Ponomarev, A.M. Kashevnik, V.G. Parfenov. Group context-driven collaborative filtering recommending systems: main principles, architecture and models.

Abstract.

The paper proposes an architecture and main models of context-driven collaborative filtering recommending systems. The major problems arising during creation of such systems are identified and their possible solutions are suggested. The advantages of contextual pre-filtering methods for context analysis are justified. The main processes of recommendation generation are described. The usage of the system is demonstrated on a case study of mobile tourist application.

Keywords:

recommending systems, collaborative filtering, context, ontology, profile management.

PP. 14-25.

Full version of the article in pdf.

REFERENCES

1. Garcia I., Sebastia L., Onaindia E., Guzman C. A. Group Recommender System for Tourist Activities // EC-Web 2009: Proceedings of E-Commerce and Web Technologies, The 10th International Conference (2009). LNCS 5692. Springer, 2009. Pp. 26–37.
2. 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, No. 6. Pp. 649–659.
3. Chen Y.-J., Chen Y.-M., Wu M.-S. An expert recommendation system for product empirical knowledge consultation // ICCSIT2010: The 3rd IEEE International Conference on Computer Science and Information Technology, IEEE, 2010. Pp. 23–27.
4. Shilov N. Gruppovyye rekomenduyushchiye sistemy dlya konfigurirovaniya gibkikh setevykh organizatsiy, Informatsionno-upravlyayushchiye sistemy, 2012, No. 5, Pp. 69–74.
5. McCarthy K., Salamo M., Coyole L., McGinty L., Smyth B., Nixon P. Group Recommender Systems: A Critiquing Based Approach // IUI '06: Proceedings of the 11th international conference on Intelligent user interfaces, 2006. Pp. 267–269.
6. Adomavicius G., Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions / IEEE Transactions on Knowledge and Data Engineering (2005), IEEE Educational Activities Department, 2005. Vol. 17, No. 6. Pp. 734–749.
7. Shilov N. Problemy podderzhki prinyatiya resheniy pri konfigurirovanii gibkikh setevykh organizatsiy. Trudy SPIIRAN, 2012, No. 22, Pp. 224–233.
8. Smirnov A., Shilov N. Collaborative Recommendation Systems for PLM: Approach and Technological Framework // Proc. of the 8th International Product Lifecycle Management Conference - PLM11 (July 11–13, 2011, Eindhoven, the Netherlands)
9. Smirnov A.V., Shilov N.G. Gruppovaya rekomendatelnaya sistema dlya upravleniya zhiznennym tsiklom izdeliy: podkhod i tekhnologii // Izvestiya YuFU. Tekhnicheskiye nauki, 2011. – No. 5 (118). Pp. 203–206.
10. Baatarjav E.-A., Phithakkitnukoon S., Dantu R. Group Recommendation System for Facebook / OTM 2008 // Proceedings of On the Move to Meaningful Internet Systems Workshop (2008), LNCS 5333, Springer, 2009. Pp. 211–219.
11. Romesburg H. C. Cluster Analysis for Researchers, Lulu Press, California, 2004. 340 p.
12. Flake G. W., Lawrence S., Giles C. L., Coetzee F. Self-Organization and identification of Web Communities // IEEE Computer, IEEE, 2002. Vol. 35, No. 3. Pp. 66–71.
13. Baeza-Yates R., Ribeiro-Neto B. Modern Information Retrieval, Addison-Wesley, 1999. 513 p.
14. Salton G. Automatic Text Processing: The Transformation Analysis and Retrieval of Information by Computer, Addison-Wesley, 1989. 543 p.
15. Belkin N., Croft B. Information Filtering and Information Retrieval // Communications of the ACM, Special issue on information filtering, ACM New York, 1992. Vol. 35, No. 12. Pp. 29–37.
16. Adomavicius G., Mobasher B., Ricci F., Tuzhilin A. Context-aware recommender systems // AI Magazine. 2011. 32(3), Pp. 67–80.
17. Adomavicius G., Sankaranarayanan R., Sen S., Tuzhilin A. Incorporating contextual information in recommender systems using a multidimensional approach // ACM Trans. Inf. Syst. January 2005. vol. 23, no. 1. Pp. 103–145.
18. Codina V., Ricci F., Ceccaroni L. Semantically-enhanced pre-filtering for context-aware recommender systems // In Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation (CaRR '13), New York, NY, USA, 2013. Pp. 15–18.
19. Baltrunas L., Ricci F. Context-based splitting of item ratings in collaborative filtering // In Proceedings of the third ACM conference on Recommender systems (RecSys '09), ACM, 2009. Pp. 245–248.
20. Baltrunas L., Ricci F. Context-Dependent Recommendations with Items Splitting // Proceedings of the 1st Italian Information Retrieval Workshop (IIR’10), January 27–28, 2010, Padua, Italy. Pp. 71–75.
21. Koren Y., Bell R., Volinsky C. Matrix factorization techniques for recommender systems // IEEE Computer, Aug 2009. vol. 42, no. 8. Pp. 30–37.
22. Rendle S. Factorization Machines with libFM // ACM Trans. on Intell. Syst. Technol., May 2012. vol. 3, no. 3. Pp. 57:1–57:22.
23. Shi Y., Larson M., Hanjalic A. Mining contextual movie similarity with matrix factorization for context-aware recommendation // ACM Trans. Intell. Syst. Technol., February 2013. vol. 4, no. 1. Pp. 16:1–16:19.
24. Smirnov A.V., Pashkin M.P., Shilov N.G., Levashova T.V. Podkhod k postroyeniyu raspredelennoy sistemy intellektualnoy podderzhki prinyatiya resheniya v otkrytoy informatsionnoy srede // Trudy SPIIRAN, 2007. No. 4. Vol. 1. Pp. 36–49.
25. Kashevnik A.M., Teslya N.N., Avtomatizirovannaya sistema sovmestnogo ispolzovaniya avtotransporta. In: Proceedings of «Informatsionnyye tekhnologii v upravlenii» (ITU-2012), 2012. Pp. 471–479.
26. Boratto, L., Carta, S., Chessa, A., Agelli, M. Group Recommendation with Automatic Identification of Users Communities. In: Proceedings of IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, 2009 (WI-IAT '09). Vol. 3, pp. 547–550, 2009.