CAPTURING USER’S PREFERENCES USING A GENETIC ALGORITHM - Determining Essential and Dispensable Item Attributes

S. Valero, E. Argente, V. Botti

Abstract

Determining the most desired product attributes would be crucial for companies that want to offer their clients those products which best fit their preferences. In this work, a genetic approach is employed for establishing the appropriate attribute weights of movies, determining which movie attributes are essential or dispensable for users in their selection process. The obtained weights are employed to predict user's ratings for a test set of movies, proving that the obtained parameters really describe their preferences.

References

  1. Goldberg, D. (1991). Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Systems, 5:139- 157.
  2. Guan, S., Ngoo, C., and Zhu, F. (2002). Handy broker: an intelligent product-brokering agent for m-commerce applications with user preference tracking. Electron. Commer. Res. Appl., 1(Issues 3-4):314-330.
  3. Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In ACM SIGIR 7899 Proceedings, pages 230-237, New York, NY, USA. ACM.
  4. Ortiz, D., Hervas, C., and Mun˜oz, J. (2001). Genetic algorithm with crossover based on confidence interval as an alternative to traditional nonlinear regression methods. In ESANN'2001 Proceedings, pages 193-198, Bruges,Belgium.
  5. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2000). Analysis of recommendation algorithms for ecommerce. In ACM EC 7800 Proceedings, pages 158- 167, New York, NY, USA. ACM Press.
  6. Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B., and Riedl, J. (1998). Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In CSCW 7898 Proceedings, pages 345-354, New York, NY, USA. ACM Press.
  7. Shardnand, U. and Maes, P. (1995). Social information filetering: Algorithms for automating “word of mouth”. In ACM CHI'95 Proceedings, pages 210-217.
  8. Shibata, H., Hoshiai, T., Kubota, M., and Teramoto, M. (6- 7 Nov. 2002). Agent technology recommending personalized information and its evaluation. In 2nd International Workshop on Autonomous Decentralized System, 2002, pages 176-183.
  9. Valero, S., Argente, E., Botti, V., Serra, J., and Corma, A. (2004a). A soft computing technique applied to industrial catalysis. In ECAI2004 Proceedings, pages 765-769. IOS Press.
  10. Valero, S., Argente, E., Botti, V., Serra, J., and Corma, A. (2004b). Soft computing techniques applied to catalytic reactions. LNAI, 3040:550-559.
  11. Valero, S., Argente, E., Botti, V., Serra, J., Serna, P., Moliner, M., and Corma, A. (2009). Doe framework for catalyst development based on soft computing techniques. Comput. Chem. Eng., 33:225-238.
Download


Paper Citation


in Harvard Style

Valero S., Argente E. and Botti V. (2010). CAPTURING USER’S PREFERENCES USING A GENETIC ALGORITHM - Determining Essential and Dispensable Item Attributes . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 599-602. DOI: 10.5220/0002727305990602


in Bibtex Style

@conference{icaart10,
author={S. Valero and E. Argente and V. Botti},
title={CAPTURING USER’S PREFERENCES USING A GENETIC ALGORITHM - Determining Essential and Dispensable Item Attributes},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={599-602},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002727305990602},
isbn={978-989-674-021-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - CAPTURING USER’S PREFERENCES USING A GENETIC ALGORITHM - Determining Essential and Dispensable Item Attributes
SN - 978-989-674-021-4
AU - Valero S.
AU - Argente E.
AU - Botti V.
PY - 2010
SP - 599
EP - 602
DO - 10.5220/0002727305990602