A CHARACTERIZATION METHODOLOGY OF EVOLUTIONARY BEHAVIOR IN RECOMMENDER SYSTEMS

Alan Cardoso, Daniel Rocha, Rafael Sachetto, Leonardo Rocha, Fernando Mourão, Wagner Meira Jr.

2011

Abstract

Recommender Systems (RSs) have become increasingly important tools for various commercial applications on theWeb. Despite numerous efforts, RSs still require improvements to make recommendation more effective and applicable to many real scenarios. Recent studies point out the temporal evolution as a primordial manner for improving RSs without, however, understand in detail how this evolution emerges. Thus, we propose a methodology for evolutive characterization of users and applications in order to provide a better understanding of this temporal dynamic in RSs. Applying our methodology in a real scenario has proved to be useful even to help in the choice of RSs adherents of each scenario.

References

  1. Abbasse, Z. and Mirrokni, V. (2007). A recommender system based on local random walks and spectral methods. In Proceedings of the 9th WebKDD and 1st SNAKDD, pages 102-108. Springer.
  2. Abbasse, Z. and Mirrokni, V. (2007). A recommender system based on local random walks and spectral methods. In Proceedings of the 9th WebKDD and 1st SNAKDD, pages 102-108. Springer.
  3. Adomavicius, G., Sankaranarayanan, R., Sen, S., and Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23(1):103-145.
  4. Adomavicius, G., Sankaranarayanan, R., Sen, S., and Tuzhilin, A. (2005). Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23(1):103-145.
  5. Adomavicius, G. and Tuzhilin, A. (2001a). Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery, 5(1):33-58.
  6. Adomavicius, G. and Tuzhilin, A. (2001a). Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery, 5(1):33-58.
  7. Adomavicius, G. and Tuzhilin, A. (2001b). Extending recommender systems: A multidimensional approach. In Proceedings of the Workshop on Intelligent Techniques for Web Personalization (ITWP2001), pages 4- 6.
  8. Adomavicius, G. and Tuzhilin, A. (2001b). Extending recommender systems: A multidimensional approach. In Proceedings of the Workshop on Intelligent Techniques for Web Personalization (ITWP2001), pages 4- 6.
  9. Adomavicius, G. and Tuzhilin, A. (2005). 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, 17(6):734-749.
  10. Adomavicius, G. and Tuzhilin, A. (2005). 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, 17(6):734-749.
  11. Anderson, C. (2006). The long tail: How endless choice is creating unlimited demand. Random House Business Books.
  12. Anderson, C. (2006). The long tail: How endless choice is creating unlimited demand. Random House Business Books.
  13. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331-470.
  14. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331-470.
  15. Cremonesi, P. and Turrin, R. (2010). Controlling Consistency in Top-N Recommender Systems. In 2010 IEEE International Conference on Data Mining Workshops, pages 919-926. IEEE.
  16. Cremonesi, P. and Turrin, R. (2010). Controlling Consistency in Top-N Recommender Systems. In 2010 IEEE International Conference on Data Mining Workshops, pages 919-926. IEEE.
  17. Koren, Y. (2009). Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD, pages 447-456. ACM New York, NY, USA.
  18. Koren, Y. (2009). Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD, pages 447-456. ACM New York, NY, USA.
  19. Lathia, N., Hailes, S., and Capra, L. (2009). Evaluating collaborative filtering over time. In In ACM SIGIR Workshop on the Future of IR Evaluation, Boston, USA.
  20. Lathia, N., Hailes, S., and Capra, L. (2009). Evaluating collaborative filtering over time. In In ACM SIGIR Workshop on the Future of IR Evaluation, Boston, USA.
  21. Lathia, N., Hailes, S., Capra, L., and Amatriain, X. (2010). Temporal diversity in recommender systems. In SIGIR 7810: Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 210-217, New York, NY, USA. ACM.
  22. Lathia, N., Hailes, S., Capra, L., and Amatriain, X. (2010). Temporal diversity in recommender systems. In SIGIR 7810: Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 210-217, New York, NY, USA. ACM.
  23. McSherry, D. (2002). Diversity-conscious retrieval. Advances in Case-Based Reasoning, pages 27-53.
  24. McSherry, D. (2002). Diversity-conscious retrieval. Advances in Case-Based Reasoning, pages 27-53.
  25. Schein, A. I., Popescul, A., Ungar, L. H., and Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th SIGIR, pages 253-260. ACM Press.
  26. Schein, A. I., Popescul, A., Ungar, L. H., and Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th SIGIR, pages 253-260. ACM Press.
  27. Stern, D. H., Herbrich, R., and Graepel, T. (2009). Matchbox: large scale online bayesian recommendations. In Proceedings of the 18th WWWW, pages 111-120, New York, NY, USA. ACM.
  28. Stern, D. H., Herbrich, R., and Graepel, T. (2009). Matchbox: large scale online bayesian recommendations. In Proceedings of the 18th WWWW, pages 111-120, New York, NY, USA. ACM.
  29. Wu, J. and Li, T. (2008). A modified fuzzy c-means algorithm for collaborative filtering. In Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, pages 1- 4. ACM.
  30. Wu, J. and Li, T. (2008). A modified fuzzy c-means algorithm for collaborative filtering. In Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, pages 1- 4. ACM.
  31. Zhang, M. and Hurley, N. (2008). Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the ACM RS, pages 123-130. ACM.
  32. Zhang, M. and Hurley, N. (2008). Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the ACM RS, pages 123-130. ACM.
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Paper Citation


in Harvard Style

Cardoso A., Rocha D., Sachetto R., Rocha L., Mourão F. and Meira Jr. W. (2011). A CHARACTERIZATION METHODOLOGY OF EVOLUTIONARY BEHAVIOR IN RECOMMENDER SYSTEMS . In Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WTM, (WEBIST 2011) ISBN 978-989-8425-51-5, pages 696-706. DOI: 10.5220/0003479306960706


in Harvard Style

Cardoso A., Rocha D., Sachetto R., Rocha L., Mourão F. and Meira Jr. W. (2011). A CHARACTERIZATION METHODOLOGY OF EVOLUTIONARY BEHAVIOR IN RECOMMENDER SYSTEMS . In Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WTM, (WEBIST 2011) ISBN 978-989-8425-51-5, pages 696-706. DOI: 10.5220/0003479306960706


in Bibtex Style

@conference{wtm11,
author={Alan Cardoso and Daniel Rocha and Rafael Sachetto and Leonardo Rocha and Fernando Mourão and Wagner Meira Jr.},
title={A CHARACTERIZATION METHODOLOGY OF EVOLUTIONARY BEHAVIOR IN RECOMMENDER SYSTEMS},
booktitle={Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WTM, (WEBIST 2011)},
year={2011},
pages={696-706},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003479306960706},
isbn={978-989-8425-51-5},
}


in Bibtex Style

@conference{wtm11,
author={Alan Cardoso and Daniel Rocha and Rafael Sachetto and Leonardo Rocha and Fernando Mourão and Wagner Meira Jr.},
title={A CHARACTERIZATION METHODOLOGY OF EVOLUTIONARY BEHAVIOR IN RECOMMENDER SYSTEMS},
booktitle={Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WTM, (WEBIST 2011)},
year={2011},
pages={696-706},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003479306960706},
isbn={978-989-8425-51-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WTM, (WEBIST 2011)
TI - A CHARACTERIZATION METHODOLOGY OF EVOLUTIONARY BEHAVIOR IN RECOMMENDER SYSTEMS
SN - 978-989-8425-51-5
AU - Cardoso A.
AU - Rocha D.
AU - Sachetto R.
AU - Rocha L.
AU - Mourão F.
AU - Meira Jr. W.
PY - 2011
SP - 696
EP - 706
DO - 10.5220/0003479306960706


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Web Information Systems and Technologies - Volume 1: WTM, (WEBIST 2011)
TI - A CHARACTERIZATION METHODOLOGY OF EVOLUTIONARY BEHAVIOR IN RECOMMENDER SYSTEMS
SN - 978-989-8425-51-5
AU - Cardoso A.
AU - Rocha D.
AU - Sachetto R.
AU - Rocha L.
AU - Mourão F.
AU - Meira Jr. W.
PY - 2011
SP - 696
EP - 706
DO - 10.5220/0003479306960706