# A New Approach for Collaborative Filtering based on Bayesian Network Inference

### Loc Nguyen

#### Abstract

Collaborative filtering (CF) is one of the most popular algorithms, for recommendation in cases, the items which are recommended to users, have been determined by relying on the outcomes done on surveying their communities. There are two main CF-approaches, which are memory-based and model-based. The model-based approach is more dominant by real-time response when it takes advantage of inference mechanism in recommendation task. However the problem of incomplete data is still an open research and the inference engine is being improved more and more so as to gain high accuracy and high speed. I propose a new model-based CF based on applying Bayesian network (BN) into reference engine with assertion that BN is an optimal inference model because BN is user’s purchase pattern and Bayesian inference is evidence-based inferring mechanism which is appropriate to rating database. Because the quality of BN relies on the completion of training data, it gets low if training data have a lot of missing values. So I also suggest an average technique to fill in missing values.

#### References

- Campos, L. M. d., Fernández-Luna, J. M., Huete, J. F. & Rueda-Morales, M. A., 2010. Combining contentbased and collaborative recommendations: A hybrid approach based on Bayesian networks. International Journal of Approximate Reasoning, September, 51(7), p. 785-799.
- GroupLens, 1998. MovieLens datasets. [Online] Available at: http://grouplens.org/datasets/movielens/ [Accessed 3 August 2012].
- Herlocker, J. L., Konstan, J. A., Terveen, L. G. & Riedl, J. T., 2004. Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems (TOIS), 22(1), pp. 5-53.
- Langseth, H., 2009. Bayesian Networks for Collaborative Filtering. s.l., Tapir Akademisk Forlag, pp. 67-78.
- Miyahara, K. & Pazzani, M. J., 2000. Collaborative Filtering with the Simple Bayesian Classifier. In: R. Mizoguchi & J. Slaney, eds. PRICAI 2000 Topics in Artificial Intelligence. s.l.:Springer Berlin Heidelberg, pp. 679-689.
- Neapolitan, R. E., 2003. Learning Bayesian Networks. Upper Saddle River(New Jersey): Prentice Hall.
- Ricci, F., Rokach, L., Shapira, B. & Kantor, P. B., 2011. Recommender Systems Handbook. s.l.:Springer New York Dordrecht Heidelberg London.
- Serafín, C. M., Carmelo, R. T., Pedro, M. L. & Francisco, V. J. D., 2003. Elvira system. 0.11 ed. s.l.:National University of Distance Education.
- Su, X. & Khoshgoftaar, T. M., 2009. A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, Volume 2009.

#### Paper Citation

#### in Harvard Style

Nguyen L. (2015). **A New Approach for Collaborative Filtering based on Bayesian Network Inference** . In *Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)* ISBN 978-989-758-158-8, pages 475-480. DOI: 10.5220/0005635204750480

#### in Bibtex Style

@conference{kdir15,

author={Loc Nguyen},

title={A New Approach for Collaborative Filtering based on Bayesian Network Inference},

booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},

year={2015},

pages={475-480},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0005635204750480},

isbn={978-989-758-158-8},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)

TI - A New Approach for Collaborative Filtering based on Bayesian Network Inference

SN - 978-989-758-158-8

AU - Nguyen L.

PY - 2015

SP - 475

EP - 480

DO - 10.5220/0005635204750480