User based Collaborative Filtering with Temporal Information for Purchase Data

Maunendra Sankar Desarkar, Sudeshna Sarkar

Abstract

User based collaborative filtering algorithms are widely used for generating recommendations for users. Standard user based collaborative filtering algorithms do not consider time as a factor while measuring the user similarities and building the recommendation list. However, users’ interests often shift with time. Recommender systems should therefore rely on recent purchases of the users to address this user dynamics. Items also have their own dynamics. Most of the items in a recommender system are widely popular just after their releases but do not sell that well afterwards. Giving more importance to the recent purchases of the experts may capture the item dynamics and hence result in better recommendation accuracy. We study the performances of different time-aware user based collaborative filtering algorithms on several benchmark datasets. The proposed algorithms use the time-of-purchase information for calculating user similarities. The time information is also used while combining the purchase behaviors of the experts and generating the final recommendation.

References

  1. Campos, P. G., Bellogín, A., Díez, F., and Chavarriaga, J. E. (2010). Simple time-biased knn-based recommendations. In Proceedings of the Workshop on ContextAware Movie Recommendation, CAMRa 7810, pages 20-23.
  2. Deshpande, M. and Karypis, G. (2004). Selective markov models for predicting web page accesses. ACM Trans. Internet Technol., 4:163-184.
  3. Ding, Y. and Li, X. (2005). Time weight collaborative filtering. In Proceedings of the 14th ACM international conference on Information and knowledge management, CIKM 7805, pages 485-492.
  4. Ding, Y., Li, X., and Orlowska, M. E. (2006). Recencybased collaborative filtering. In Proceedings of the 17th Australasian Database Conference - Volume 49, ADC 7806, pages 99-107.
  5. Herlocker, J., Konstan, J. A., and Riedl, J. (2002). An empirical analysis of design choices in neighborhoodbased collaborative filtering algorithms. Inf. Retr., 5:287-310.
  6. Kawamae, N., Sakano, H., and Yamada, T. (2009). Personalized recommendation based on the personal innovator degree. In Proceedings of the third ACM conference on Recommender systems, RecSys 7809, pages 329-332.
  7. Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 7808, pages 426-434.
  8. Koren, Y. (2009). Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 7809, pages 447-456.
  9. Lee, T. Q., Park, Y., and Park, Y.-T. (2008). A time-based approach to effective recommender systems using implicit feedback. Expert Syst. Appl., 34:3055-3062.
  10. Parameswaran, A. G., Koutrika, G., Bercovitz, B., and Garcia-Molina, H. (2010). Recsplorer: recommendation algorithms based on precedence mining. In Proceedings of the 2010 international conference on Management of data, SIGMOD 7810, pages 87-98.
  11. Rendle, S., Freudenthaler, C., and Lars, S.-T. (2010). Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web, WWW 7810, pages 811-820.
  12. Rongfei, J., Maozhong, J., and Chao, L. (2010). Using temporal information to improve predictive accuracy of collaborative filtering algorithms. In Proceedings of the 2010 12th International Asia-Pacific Web Conference, APWEB 7810, pages 301-306.
  13. Shani, G., Heckerman, D., and Brafman, R. I. (2005). An mdp-based recommender system. J. Mach. Learn. Res., 6:1265-1295.
  14. Zheng, N. and Li, Q. (2011). A recommender system based on tag and time information for social tagging systems. Expert Syst. Appl., 38:4575-4587.
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Paper Citation


in Harvard Style

Desarkar M. and Sarkar S. (2012). User based Collaborative Filtering with Temporal Information for Purchase Data . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 55-64. DOI: 10.5220/0004134400550064


in Bibtex Style

@conference{kdir12,
author={Maunendra Sankar Desarkar and Sudeshna Sarkar},
title={User based Collaborative Filtering with Temporal Information for Purchase Data},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={55-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004134400550064},
isbn={978-989-8565-29-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - User based Collaborative Filtering with Temporal Information for Purchase Data
SN - 978-989-8565-29-7
AU - Desarkar M.
AU - Sarkar S.
PY - 2012
SP - 55
EP - 64
DO - 10.5220/0004134400550064