Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

, , ,

DOI: http://dx.doi.org/10.15439/2016F428

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 411–420 ()

Full text

Abstract. Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort is required to change the method used. In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data). Our approach can be used with existing recommendation algorithms. It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction.

References

  1. F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Springer US, 2011, pp. 1–35. ISBN 978-0-387-85819-7. [Online]. Available: http://dx.doi.org/10.1007/978-0-387-85820-3_1
  2. G. Adomavicius and A. Tuzhilin, Recommender Systems Handbook. Boston, MA: Springer US, 2011, ch. Context-Aware Recommender Systems, pp. 217–253. ISBN 978-0-387-85820-3. [Online]. Available: http://dx.doi.org/10.1007/978-0-387-85820-3_7
  3. K. Goczyla, W. Waloszek, and A. Waloszek, “Contextualization of a DL knowledge base,” in Proc. of the 2007 Int. Workshop on Description Logics (DL2007), 2007. [Online]. Available: http://ceur-ws.org/Vol-250/paper_55.pdf
  4. C. Bettini, O. Brdiczka, K. Henricksen, J. Indulska, D. Nicklas, A. Ranganathan, and D. Riboni, “A survey of context modelling and reasoning techniques,” Pervasive and Mobile Computing, vol. 6, no. 2, pp. 161 – 180, 2010. http://dx.doi.org/10.1016/j.pmcj.2009.06.002 Context Modelling, Reasoning and Management. [Online]. Available: http://dx.doi.org/10.1016/j.pmcj.2009.06.002
  5. C. Bolchini, C. A. Curino, E. Quintarelli, F. A. Schreiber, and L. Tanca, “A data-oriented survey of context models,” SIGMOD Rec., vol. 36, no. 4, pp. 19–26, Dec. 2007. http://dx.doi.org/10.1145/1361348.1361353. [Online]. Available: http://dx.doi.org/10.1145/1361348.1361353
  6. R. Krummenacher and T. Strang, “Ontology-based context modeling,” in In Workshop on Context-Aware Proactive Systems, 2007.
  7. J. Ye, L. Coyle, S. Dobson, and P. Nixon, “Ontology-based models in pervasive computing systems,” Knowl. Eng. Rev., vol. 22, no. 4, pp. 315–347, Dec. 2007. http://dx.doi.org/10.1017/S0269888907001208. [Online]. Available: http://dx.doi.org/10.1017/S0269888907001208
  8. L. Costabello, Context-Aware Access Control and Presentation for Linked Data, 2013, ch. A Declarative Model for Mobile Context, pp. 21–32.
  9. T. Heath and C. Bizer, Linked Data: Evolving the Web into a Global Data Space, 1st ed. Morgan & Claypool, 2011. ISBN 9781608454303. [Online]. Available: http://dx.doi.org/10.2200/S00334ED1V01Y201102WBE001
  10. H. Chen, T. Finin, and A. Joshi, Ontologies for Agents: Theory and Experiences. Basel: Birkhäuser Basel, 2005, ch. The SOUPA Ontology for Pervasive Computing, pp. 233–258. ISBN 978-3-7643-7361-0. [Online]. Available: http://dx.doi.org/10.1007/3-7643-7361-X_10
  11. T. Strang, C. Linnhoff-Popien, and K. Frank, Distributed Applications and Interoperable Systems: 4th IFIP WG6.1 Int. Conf., DAIS 2003, Paris, France, November 17-21, 2003. Proc. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, ch. CoOL: A Context Ontology Language to Enable Contextual Interoperability, pp. 236–247. ISBN 978-3-540-40010-3. [Online]. Available: http://dx.doi.org/10.1007/978-3-540-40010-3_21
  12. X. H. Wang, D. Q. Zhang, T. Gu, and H. K. Pung, “Ontology based context modeling and reasoning using owl,” in Proc. of the Second IEEE Annual Conf. on Pervasive Computing and Communications Workshops, ser. PERCOMW ’04. Washington, DC, USA: IEEE Computer Society, 2004. http://dx.doi.org/10.1109/PERCOMW.2004.1276898. ISBN 0-7695-2106-1 pp. 18–. [Online]. Available: http://dx.doi.org/10.1109/PERCOMW.2004.1276898
  13. D. Preuveneers, J. Bergh, D. Wagelaar, A. Georges, P. Rigole, T. Clerckx, Y. Berbers, K. Coninx, V. Jonckers, and K. Bosschere, Ambient Intelligence: Second European Symposium, EUSAI 2004, Eindhoven, The Netherlands, November 8-11, 2004. Proc. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, ch. Towards an Extensible Context Ontology for Ambient Intelligence, pp. 148–159. ISBN 978-3-540-30473-9. [Online]. Available: http://dx.doi.org/10.1007/978-3-540-30473-9_15
  14. P. Korpipää and J. Mäntyjärvi, Modeling and Using Context: 4th Int. and Interdisciplinary Conf. CONTEXT 2003 Stanford, CA, USA, June 23–25, 2003 Proc. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, ch. An Ontology for Mobile Device Sensor-Based Context Awareness, pp. 451–458. ISBN 978-3-540-44958-4. [Online]. Available: http://dx.doi.org/10.1007/3-540-44958-2_37
  15. R. Hervás and J. Bravo, “Towards the ubiquitous visualization: Adaptive user-interfaces based on the semantic web,” Interacting with Computers, vol. 23, no. 1, pp. 40 – 56, 2011. http://dx.doi.org/http://dx.doi.org/10.1016/j.intcom.2010.08.002. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0953543810000676
  16. G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, and P. Steggles, “Towards a better understanding of context and context-awareness,” in Proc. of the 1st Int. Symposium on Handheld and Ubiquitous Computing, ser. HUC ’99. London, UK, UK: Springer-Verlag, 1999. ISBN 3-540-66550-1 pp. 304–307. [Online]. Available: http://dx.doi.org/10.1007/3-540-48157-5_29
  17. M. Kaminskas and F. Ricci, “Contextual music information retrieval and recommendation: State of the art and challenges,” Computer Science Review, vol. 6, no. 2–3, pp. 89 – 119, 2012. http://dx.doi.org/10.1016/j.cosrev.2012.04.002.
  18. R. Cai, C. Zhang, C. Wang, L. Zhang, and W.-Y. Ma, “Musicsense: Contextual music recommendation using emotional allocation modeling,” in Proc. of the 15th ACM Int. Conf. on Multimedia, ser. MM ’07. New York, NY, USA: ACM, 2007. http://dx.doi.org/10.1145/1291233.1291369. ISBN 978-1-59593-702-5 pp. 553–556. [Online]. Available: http://dx.doi.org/10.1145/1291233.1291369
  19. L. Baltrunas, B. Ludwig, S. Peer, and F. Ricci, “Context relevance assessment and exploitation in mobile recommender systems,” Personal Ubiquitous Comput., vol. 16, no. 5, pp. 507–526, Jun. 2012. http://dx.doi.org/10.1007/s00779-011-0417-x.
  20. Z. Su, J. Yan, H. Ling, and H. Chen, “Research on personalized recommendation algorithm based on ontological user interest model,” J. of Computational Information Systems, vol. 8, no. 1, pp. 169–181, Jan. 2012.
  21. C. Rack, S. Arbanowski, and S. Steglich, “Context-aware, Ontology- based Recommendations,” in SAINT-W ’06: Proc. of the Int. Symposium on Applications on Internet Workshops. Washington, DC, USA: IEEE Computer Society, 2006. http://dx.doi.org/10.1109/saint-w.2006.13. ISBN 0769525105 pp. 98–104.
  22. I. Cantador, A. Bellogín, and P. Castells, “Ontology-based personalised and context-aware recommendations of news items,” in Proc. of the 2008 IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology - Volume 01, ser. WI-IAT ’08. Washington, DC, USA: IEEE Computer Society, 2008. http://dx.doi.org/10.1109/WIIAT.2008.204. ISBN 978-0-7695-3496-1 pp. 562–565.
  23. J. Rodríguez, M. Bravo, and R. Guzmán, “Multidimensional ontology model to support context-aware systems,” 2013. [Online]. Available: http://www.aaai.org/ocs/index.php/WS/AAAIW13/paper/view/7187
  24. A. Hawalah and M. Fasli, “Utilizing contextual ontological user profiles for personalized recommendations,” Expert Systems with Applications, vol. 41, no. 10, pp. 4777 – 4797, 2014. doi: http://dx.doi.org/10.1016/j.eswa.2014.01.039. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957417414000633
  25. S. E. Middleton, N. R. Shadbolt, and D. C. De Roure, “Ontological user profiling in recommender systems,” ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 54–88, Jan. 2004. http://dx.doi.org/10.1145/963770.963773.
  26. B. Mobasher, X. Jin, and Y. Zhou, Web Mining: From Web to Semantic Web: First European Web Mining Forum, EWMF 2003, Invited and Selected Revised Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004, ch. Semantically Enhanced Collaborative Filtering on the Web, pp. 57–76. ISBN 978-3-540-30123-3. [Online]. Available: http://dx.doi.org/10.1007/978-3-540-30123-3_4
  27. S. S. Anand, P. Kearney, and M. Shapcott, “Generating semantically enriched user profiles for web personalization,” ACM Trans. Internet Technol., vol. 7, no. 4, Oct. 2007. http://dx.doi.org/10.1145/1278366.1278371.
  28. P. Lops, M. de Gemmis, and G. Semeraro, Recommender Systems Handbook. Boston, MA: Springer US, 2011, ch. Content-based Recommender Systems: State of the Art and Trends, pp. 73–105. ISBN 978-0-387-85820-3. [Online]. Available: http://dx.doi.org/10.1007/978-0-387-85820-3_3
  29. T. Di Noia and V. C. Ostuni, Reasoning Web. Web Logic Rules: 11th Int. Summer School 2015, Berlin, Germany, July 31-August 4, 2015, Tutorial Lectures. Cham: Springer International Publishing, 2015, ch. Recommender Systems and Linked Open Data, pp. 88–113. ISBN 978-3-319-21768-0. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-21768-0_4
  30. A. K. Dey, “Understanding and using context,” Personal Ubiquitous Comput., vol. 5, no. 1, pp. 4–7, Jan. 2001. http://dx.doi.org/10.1007/s007790170019.
  31. M. Fernández-López, A. Gómez-Pérez, and N. Juristo, “Methontology: from ontological art towards ontological engineering,” in Proc. Sympo- sium on Ontological Engineering of AAAI, 1997.
  32. D. Heckmann, T. Schwartz, B. Brandherm, M. Schmitz, and M. Wilamowitz-Moellendorff, User Modeling 2005: 10th Int. Conf., UM 2005, Edinburgh, Scotland, UK, July 24-29, 2005. Proc. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, ch. Gumo – The General User Model Ontology, pp. 428–432. ISBN 978-3-540-31878-1. [Online]. Available: http://dx.doi.org/10.1007/11527886_58
  33. J. A. Russell, “A circumplex model of affect.” J. of Personality and Social Psychology, vol. 39, no. 6, pp. 1161–1178, Dec. 1980. http://dx.doi.org/10.1037/h0077714.
  34. A. Mehrabian, “Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament,” Cur- rent Psychology, vol. 14, no. 4, pp. 261–292. http://dx.doi.org/10.1007/BF02686918.
  35. K. Goczyła, A. Waloszek, W. Waloszek, and T. Zawadzka, Intelligent Tools for Building a Scientific Information Platform. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, ch. Modularized Knowledge Bases Using Contexts, Conglomerates and a Query Language, pp. 179–201. ISBN 978-3-642-24809-2. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-24809-2_11
  36. K. Goczyla, A. Waloszek, and W. Waloszek, “Towards context- semantic knowledge bases,” in Federated Conf. on Computer Science and Information Systems - FedCSIS 2012, Wroclaw, Poland, 9-12 September 2012, Proc., M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2012. ISBN 978-83-60810-51-4 pp. 475–482. [Online]. Available: https://fedcsis.org/proceedings/2012/pliks/388.pdf
  37. A. Karpus and K. Goczyla, “A multi-domain hybrid recommender systems based on a dynamic contextual ontological user profile,” in Doctoral Consortium (IC3K 2014), 2014. doi: 10.5220/0005174300830087. ISBN Not Available pp. 83–87. [Online]. Available: http://www.scitepress.org/DigitalLibrary/PublicationsDetail.aspx?ID=6Kh9MDlu7qs=&t=1
  38. P. Adamopoulos and A. Tuzhilin, “Estimating the Value of Multi- Dimensional Data Sets in Context-based Recommender Systems,” in 8th ACM Conf. on Recommender Systems (RecSys 2014), 2014.
  39. Y. Koren, “Collaborative filtering with temporal dynamics,” in Proc. of the 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, ser. KDD ’09. New York, NY, USA: ACM, 2009. http://dx.doi.org/10.1145/1557019.1557072. ISBN 978-1-60558-495-9 pp. 447–456.