Article

A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion

Authors:

Yoichi Motomura,

Hideki AsohAuthors Info & Claims

UM '07: Proceedings of the 11th international conference on User Modeling

Pages 247 - 257

https://doi.org/10.1007/978-3-540-73078-1_28

Published: 25 July 2007 Publication History

Abstract

This paper proposes a novel approach for constructing users' movie preference models using Bayesian networks. The advantages of the constructed preference models are 1) consideration of users' context in addition to users' personality, 2) multiple applications, such as recommendation and promotion. Data acquisition process through a WWW questionnaire survey and a Bayesian network model construction process using the data are described. The effectiveness of the constructed model in terms of recommendation and promotion is also demonstrated through experiments.

References

[1]

Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Engineering 17(6), 734-749 (2005).

Digital Library

[2]

Binder, J., Koller, D., Russell, S., Kanazawa, K.: Adaptive probabilistic networks with hidden variables. Machine Learning 29, 213-244 (1997).

Digital Library

[3]

Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 43-52 (1998).

Digital Library

[4]

Burke, R.: Hybrid recommender systems: survey and experiments. User-Modeling and User-Adapted Interactions 12, 331-370 (2002).

Digital Library

[5]

de Campos, L.M.: Independency relationships and learning algorithms for singly connected networks. Journal of Experimental and Theoretical Artificial Intelligence 10, 511-549 (1998).

[6]

Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309-347 (2002).

Digital Library

[7]

Herlocker, J., et al.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5-53 (2004).

Digital Library

[8]

Horvitz, E.: Principles of mixed-initiative user interfaces. In: Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (1999).

Digital Library

[9]

Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2001).

Digital Library

[10]

Jensen, F.V., et al.: The SACSO methodology for troubleshooting complex systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM) 15, 321-333 (2001).

Digital Library

[11]

Mani, S., McDermott, S., Valtorta, M.: MENTOR: A Bayesian model of prediction of mental retardation in newborns, Research in Developmental Disabilities, vol. 8(5) (1997).

[12]

Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the Web. In: Berendt, B., Hotho, A., Mladenic, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS (LNAI), vol. 3209, Springer, Heidelberg (2004).

[13]

Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings. of the 5th ACM Conference on Digital Libraries, pp. 195- 204 (2000).

Digital Library

[14]

Motomura, Y.: Bayesian network construction system: BAYONET. In: Proceedings of Tutorial on Bayesian Networks, pp. 54-58 (In Japanese) (2001).

[15]

Ono, C., Motomura, Y., Asoh, H.: A study of probabilistic models for integrating collaborative and content-based recommendation, Working Notes of IJCAI-05 Workshop on Advances in Preference Handling (2005).

[16]

Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Francisco (1988).

Digital Library

[17]

Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 56-58 (1997).

Digital Library

[18]

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of ACM Conference on Computer Supported Cooperative Work, pp. 175-186. ACM Press, New York (1994).

Digital Library

[19]

Shardanand, U., Maes, P.: Social information filtering: algorithms for automating word of mouth. In: Proceedings of CHI'95 Mosaic of Creativity, pp. 210-217 (1995).

Digital Library

[20]

Zekerman, I., Alberecht, D.W.: Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction 11(1-2), 5-18 (2001).

Digital Library

[21]

Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236-244 (1963).

Cited By

Champiri ZFisher BChong C(2021)A Contextual Bayesian User Experience Model for Scholarly Recommender SystemsArtificial Intelligence in HCI10.1007/978-3-030-77772-2_10(139-165)Online publication date: 24-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-77772-2_10
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cover image Guide Proceedings

UM '07: Proceedings of the 11th international conference on User Modeling

July 2007

484 pages

ISBN:9783540730774

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 25 July 2007

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Cited By

Champiri ZFisher BChong C(2021)A Contextual Bayesian User Experience Model for Scholarly Recommender SystemsArtificial Intelligence in HCI10.1007/978-3-030-77772-2_10(139-165)Online publication date: 24-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-77772-2_10
Wang YWang MXu W(2018)A Sentiment-Enhanced Hybrid Recommender System for Movie RecommendationWireless Communications & Mobile Computing10.1155/2018/82637042018(33)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1155/2018/8263704
Ramnani RSengupta SDebnath PBogdanovych ARichards DSimoff SPelachaud CHeylen DTrescak T(2018)Intelligent Travel AdvisorProceedings of the 18th International Conference on Intelligent Virtual Agents10.1145/3267851.3267885(341-342)Online publication date: 5-Nov-2018
https://dl.acm.org/doi/10.1145/3267851.3267885
Zheng YSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)Context suggestionProceedings of the International Conference on Web Intelligence10.1145/3106426.3106466(753-760)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106466
Khoshkangini RPini MRossi F(2016)A Self-Adaptive Context-Aware Group Recommender SystemAI*IA 2016 Advances in Artificial Intelligence10.1007/978-3-319-49130-1_19(250-265)Online publication date: 29-Nov-2016
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Ying JKuo WTseng VLu E(2014)Mining User Check-In Behavior with a Random Walk for Urban Point-of-Interest RecommendationsACM Transactions on Intelligent Systems and Technology10.1145/25230685:3(1-26)Online publication date: 16-Sep-2014
https://dl.acm.org/doi/10.1145/2523068
Babas KChalkiadakis GTripolitakis EYang QKing ILi QPu PKarypis G(2013)You are what you consumeProceedings of the 7th ACM conference on Recommender systems10.1145/2507157.2507158(221-228)Online publication date: 12-Oct-2013
https://dl.acm.org/doi/10.1145/2507157.2507158
Ying JLu EKuo WTseng VWolfson OZheng Y(2012)Urban point-of-interest recommendation by mining user check-in behaviorsProceedings of the ACM SIGKDD International Workshop on Urban Computing10.1145/2346496.2346507(63-70)Online publication date: 12-Aug-2012
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Hijikata YKai YNishida SOssowski SLecca P(2012)The relation between user intervention and user satisfaction for information recommendationProceedings of the 27th Annual ACM Symposium on Applied Computing10.1145/2245276.2232109(2002-2007)Online publication date: 26-Mar-2012
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Modani NGupta RNagar SShannigrahi SGoyal SDey K(2012)Like-Minded communitiesProceedings of the 13th international conference on Web Information Systems Engineering10.1007/978-3-642-35063-4_28(382-395)Online publication date: 28-Nov-2012
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