Incorporating Context into Recommender Systems Using Multidimensional Rating Estimation Methods

Gediminas Adomavicius, Alexander Tuzhilin

2005

Abstract

Traditionally recommendation technologies have been focusing on recommending items to users (or users to items) and typically do not consider additional contextual information, such as time or location. In this paper we discuss a multidimensional approach to recommender systems that supports additional dimensions capturing the context in which recommendations are made. One of the most important questions in recommender systems research is how to estimate unknown ratings, and in the paper we address this issue for the multidimensional recommendation space. We present the classification of multi- dimensional rating estimation methods, discuss how to extend traditional two-dimensional recommendation approaches to the multidimensional space, and identify research directions for the multidimensional rating estimation problem.

References

  1. Adomavicius, G., R. Sankaranarayanan, S. Sen, A. Tuzhilin. Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach. ACM Transactions on Information Systems, 23(1):103-145, 2005.
  2. Adomavicius, G., A. Tuzhilin. Towards 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, 2005.
  3. Ansari, A., S. Essegaier, R. Kohli. Internet recommendations systems. Journal of Marketing Research, pages 363-375, August 2000.
  4. Balabanovic, M., Y. Shoham. Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3):66-72, 1997.
  5. Basu, C., H. Hirsh, W. Cohen. Recommendation as Classification: Using Social and Content-Based Information in Recommendation. Proc. of the 15th National Conf. on AI, 1998.
  6. Breese, J. S., D. Heckerman, C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conf. on Uncertainty in AI, 1998.
  7. Burke, R. Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4):331-370, 2002.
  8. Chaudury, S., U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1):65-74, 1997.
  9. Delgado, J., N. Ishii. Memory-based weighted-majority prediction for recommender systems. In ACM SIGIR'99 Workshop on Recommender Systems: Algorithms and Evaluation.
  10. Mooney, R. J., L. Roy. Content-based book recommending using learning for text categorization. Proceedings of the 5th ACM Conference on Digital Libraries, pp. 195-204, 2000.
  11. Resnick, P., N. Iakovou, M. Sushak, P. Bergstrom, J. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of CSCW'94 Conference, 1994.
  12. Sarwar, B., G. Karypis, J. Konstan, J. Riedl. Item-based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th International WWW Conference, 2001.
  13. Schafer, J. B., J. A. Konstan, J. Riedl. E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1/2):115-153, 2001.
  14. Shardanand, U., P. Maes. Social information filtering: Algorithms for automating 'word of mouth'. Proceedings of CHI'95 Conference, 1995.
  15. Soboroff, I., C. Nicholas. Combining content and collaboration in text filtering. In IJCAI'99 Workshop: Machine Learning for Information Filtering, 1999.
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Paper Citation


in Harvard Style

Adomavicius G. and Tuzhilin A. (2005). Incorporating Context into Recommender Systems Using Multidimensional Rating Estimation Methods . In Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005) ISBN 972-8865-38-4, pages 3-13. DOI: 10.5220/0001421700030013


in Bibtex Style

@conference{wprsiui05,
author={Gediminas Adomavicius and Alexander Tuzhilin},
title={Incorporating Context into Recommender Systems Using Multidimensional Rating Estimation Methods},
booktitle={Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005)},
year={2005},
pages={3-13},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001421700030013},
isbn={972-8865-38-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005)
TI - Incorporating Context into Recommender Systems Using Multidimensional Rating Estimation Methods
SN - 972-8865-38-4
AU - Adomavicius G.
AU - Tuzhilin A.
PY - 2005
SP - 3
EP - 13
DO - 10.5220/0001421700030013