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Get to the Bottom: Causal Analysis for User Modeling

Published: 09 July 2017 Publication History

Abstract

Weather affects our mood and behavior, and through them, many aspects of our life. When it is sunny, people become happier and smile, but when it rains, some get depressed. Despite this evidence and the abundance of weather data, weather has mostly been overlooked in the machine learning and data science research. This work shows how causal analysis can be applied to discover the effects of weather on TV watching patterns and how it can be applied for user modeling. We make several contributions. First, we show that some weather attributes, e.g., pressure and precipitation, cause significant changes in TV watching patterns. Second, we compare the results obtained for different levels of user granularity and different types of users. This showcases that causal analysis can be a valuable tool in user modeling. To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.

References

[1]
G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. In Recommender Systems Handbook, pages 191--226. 2015.
[2]
L. Baltrunas, B. Ludwig, S. Peer, and F. Ricci. Context relevance assessment and exploitation in mobile recommender systems. Personal Ubiquitous Comput., 16(5):507--526, June 2012.
[3]
A. Barker, K. Hawton, J. Fagg, and C. Jennison. Seasonal and weather factors in parasuicide. The British Journal of Psychiatry, 165(3):375--380, 1994.
[4]
G. A. Barnett, H.-J. Chang, E. L. Fink, and W. D. Richards. Seasonality in television viewing a mathematical model of cultural processes. Communication Research, 18(6):755--772, 1991.
[5]
M. Braunhofer and F. Ricci. Contextual Information Elicitation in Travel Recommender Systems, pages 579--592. Springer International Publishing, Cham, 2016.
[6]
M. Caliendo and S. Kopeinig. Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1):31--72, 2008.
[7]
E. G. Cohn. Weather and crime. British Journal of Criminology, 30(1):51--64, 1990.
[8]
T. S. David Hirshleifer. Good day sunshine: Stock returns and the weather. The Journal of Finance, 58(3):1009--1032, 2003.
[9]
S. Dernbach, N. Taft, J. Kurose, U. Weinsberg, C. Diot, and A. Ashkan. Cache content-selection policies for streaming video services. In 35th Annual IEEE International Conference on Computer Communications, INFOCOM 2016, San Francisco, CA, USA, April 10--14, 2016, pages 1--9, 2016.
[10]
A. Farahat and M. C. Bailey. How effective is targeted advertising? In Proceedings of the 21st International Conference on World Wide Web, pages 111--120. ACM, 2012.
[11]
R. M. Gray and D. L. Neuhoff. Quantization. IEEE Trans. Inform. Theory, 44(6):2325--29, 1998.
[12]
K. Hirano and G. W. Imbens. The propensity score with continuous treatments. In Applied Bayesian Modeling and Causal Inference from Incomplete-data Perspectives. Wiley, 2004.
[13]
G. King and R. Nielsen. Why propensity scores should not be used for matching. Working paper, 2016.
[14]
Y. Koren and R. M. Bell. Advances in collaborative filtering. In Recommender Systems Handbook, pages 77--118. 2015.
[15]
E. L. Lehmann and J. P. Romano. Testing statistical hypotheses. Springer Texts in Statistics. Springer, New York, third edition, 2005.
[16]
S. Li, N. Vlassis, J. Kawale, and Y. Fu. Matching via dimensionality reduction for estimation of treatment effects in digital marketing campaigns. In Proceedings of the 25th International Joint Conference on Artificial Intelligence, 2016.
[17]
D. Liang, L. Charlin, J. McInerney, and D. M. Blei. Modeling user exposure in recommendation. In Proceedings of the 25th International Conference on World Wide Web, WWW '16, pages 951--961, Republic and Canton of Geneva, Switzerland, 2016. International World Wide Web Conferences Steering Committee.
[18]
S. L. Morgan and C. Winship. Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge University Press, 2014.
[19]
K. B. Murray, F. Di Muro, A. Finn, and P. P. Leszczyc. The effect of weather on consumer spending. Journal of Retailing and Consumer Services, 17(6):512--520, 2010.
[20]
A. G. Parsons. The association between daily weather and daily shopping patterns. Australasian Marketing Journal (AMJ), 9(2):78--84, 2001.
[21]
T. Partonen and J. Lönnqvist. Seasonal affective disorder. The Lancet, 352(9137):1369--1374, 1998.
[22]
J. Pearl. Causality. Cambridge University Press, 2009.
[23]
K. Roe and H. Vandebosch. Weather to view or not: That is the question. European Journal of Communication, 11(2):201--216, 1996.
[24]
D. B. Rubin. Matching to remove bias in observational studies. Biometrics, pages 159--183, 1973.
[25]
D. B. Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5):688--701, 1974.
[26]
E. M. Saunders. Stock prices and wall street weather. The American Economic Review, 83(5):1337--1345, 1993.
[27]
P. Spirtes, C. N. Glymour, and R. Scheines. Causation, Prediction, and Search. MIT press, 2000.
[28]
M. Xu, S. Berkovsky, S. Ardon, S. Triukose, A. Mahanti, and I. Koprinska. Catch-up TV recommendations: show old favourites and find new ones. In ACM Conference on Recommender Systems, pages 285--294, 2013.
[29]
S. Zong, B. Kveton, S. Berkovsky, A. Ashkan, N. Vlassis, and Z. Wen. Does weather matter?: Causal analysis of TV logs. In Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, April 3--7, 2017, pages 883--884, 2017.

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cover image ACM Conferences
UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
July 2017
420 pages
ISBN:9781450346351
DOI:10.1145/3079628
  • General Chairs:
  • Maria Bielikova,
  • Eelco Herder,
  • Program Chairs:
  • Federica Cena,
  • Michel Desmarais
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 July 2017

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  1. causal analysis
  2. user modeling
  3. weather

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UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;
Overall Acceptance Rate 162 of 633 submissions, 26%

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