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Content recommendation on web portals

Published: 01 June 2013 Publication History

Abstract

How to offer recommendations to users when they have not specified what they want.

References

[1]
Adomavicius, G. and 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 (June 2005), 734--749.
[2]
Agarwal, D. and Chen, B.-C. Regression-based latent factor models. In Proceedings of KDD (2009). ACM Press, New York, 19--28.
[3]
Agarwal, D., Chen, B.-C. and Elango, P. Explore/exploit schemes for web content optimization. In Proceedings of ICDM (2009), 1--10.
[4]
Agarwal, D., Chen, B.-C. and Elango, P. Fast online learning through offine initialization for time-sensitive recommendation. In Proceedings of KDD (2010). ACM Press, New York, 703--712.
[5]
Agarwal, D., Chen, B.-C., Elango, P., Motgi, N., Park, S.-T., Ramakrishnan, R., Roy, S. and Zachariah, J. Online models for content optimization. In Proceedings of NIPS (2008).
[6]
Agarwal, D., Chen, B.-C., Elango, P. and Wang, X. Click shaping to optimize multiple objectives. In Proceedings of KDD (2011). ACM Press, New York, 132--140.
[7]
Auer, P., Cesa-Bianchi, N. and Fischer, P. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 2002.
[8]
Das, A., Datar, M., Garg, A. and Rajaram, S. Google news personalization: scalable online collaborative filtering. In Proceedings of WWW (2007). ACM Press, New York, 271--280.
[9]
Das, A., Datar, M., Garg, A, and Rajaram, S. Google news personalization: scalable online collaborative filtering. In Proceedings of WWW (2007).
[10]
Davidson, J., Liebald, B., Liu, J., Nandy, P. Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B. and Sampath, D. The youtube video recommendation system. In ACM Conference on Recommender Systems (2010). ACM Press, New York, 293--296.
[11]
Gittins, J. Bandit processes and dynamic allocation indices. J. of the Royal Statistical Society B 41 (1979).
[12]
GroupLens Research. Movielens Data Sets; http://www.grouplens.org/node/73.
[13]
Hastie, T., Tibshirani, R. and Friedman, J. The Elements of Statistical Learning. Springer, 2009.
[14]
Jambor, T. and J. Wang, J. Optimizing multiple objectives in collaborative ltering. In Proceedings of the 4th ACM conference on Recommender systems (2010)., ACM Press, New York, 55--62.
[15]
Kaggle. http://www.kaggle.com.
[16]
Kakade, S., Shalev-Shwartz, S. and Tewari, A. Efficient bandit algorithms for online multiclass prediction. In Proceedings of ICML (2008). ACM Press, New York, 440--447.
[17]
Kocsis, L. and Szepesvari, C. Bandit based Monte-Carlo planning. Machine Learning: ECML, Lecture Notes in Computer Science. Springer, 2006, 282--293.
[18]
Kohavi, R., Longbotham, R., Sommerfield, D. and Henne, R. Controlled experiments on the Web: survey and practical guide. Data Mining and Knowledge Discovery 18 (2009), 140--181. 10.1007/s10618-008-0114-1.
[19]
Koren, Y., Bell, R. and Volinsky, C. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009) 30--37.
[20]
Langford, J. and Zhang, T. The epoch-greedy algorithm for contextual multi-armed bandits. In Proceedings of NIPS, (2007).
[21]
Li, L., Chu, W., Langford, J. and Schapire, R. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th International Conference on World Wide Web. ACM Press, New York (2010) 661--670.
[22]
Li, L., Chu, W., Langford, J. and Wang, X. Unbiased offine evaluation of contextual-bandit-based news article recommendation algorithms. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (2011). ACM Press, New York, 297--306.
[23]
Linden, G. In http://glinden.blogspot.com/2008/01/brief-history-of-findory.html.
[24]
Linden, G., Smith, B. and York, J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7, 1 (2003), 76--80.
[25]
Liu, J., Dolan, P. and Pedersen, E.R. Personalized news recommendation based on click behavior. In Proceedings of the 15th International Conference on Intelligent User Interfaces (2010), 31--40.
[26]
Lv, Y., Moon, T., Kolari, P., Zheng, Z., Wang, X. and Chang, Y. Learning to model relatedness for news recommendation. In Proceedings of WWW (2011). ACM Press, New York, 57--66.
[27]
Mondaynote. In http://www.mondaynote.com/2009/02/15/recommendation-engines-a-must-for-news-sites, 2009.
[28]
Nelder, J. and Wedderburn, R. Generalized linear models. Journal of the Royal Statistical Society. Series A (General) 135 (1972) 370--384.
[29]
Pandey, S., Agarwal, D., Chakrabarti, D. and Josifovski, V. Bandits for taxonomies: A model-based approach. In Proceedings of SIAM International Conference on Data Mining (2007).
[30]
Pole, A. West, M. and Harrison, P.J. Applied Bayesian Forecasting & Time Series Analysis. Chapman-Hall, 1994.
[31]
Rendle, S. Freudenthaler, C. and Schmidt-Thieme, L. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of WWW (2010), ACM Press, New York, 811--820.
[32]
Robbins, H. Some aspects of the sequential design of experiments. Bull. Amer. Math. Soc. 58 (1952), 527--535.
[33]
Stern, D., Herbrich, R. and Graepel, T. Matchbox: Large scale online bayesian recommendations. In Proceedings of WWW (2009). ACM Press, New York, 111--120.
[34]
Steuer, R. Multi-criteria Optimization: Theory, Computation and Application. Wiley, 1986.
[35]
Svore, K.M., Volkovs, M.N. and Burges, C.J. Learning to rank with multiple objective functions. In Proceedings of the 20th International Conference on World Wide Web (2011). ACM Press, New York, 367--376.
[36]
Szabo, G. and Huberman, B.A. Predicting the popularity of online content. Commun. ACM 53, 8 (Aug. 2010), 80--88.
[37]
Thompson, W.R. On the likelihood that one unknown probability exceeps another in view of the evidence of two samples. Biometrika 25, 3-4 (1933), 285--294.
[38]
Witten, I.H., Frank, E. and Hall, M.A. Data Mining: Practical Machine Learning Tools and Techniques (3rd Edition). Morgan Kaufmann, Burlington, MA.
[39]
Yahoo! Academic Relations. R6A--Yahoo! Front Page Today Module User Click Log Dataset, Version 1.0; http://Webscope.sandbox.yahoo.com, 2012.
[40]
Yahoo! Academic Relations. Yahoo! webscope rating datasets; http://webscope.sandbox.yahoo.com/catalog.php?datatype=r, 2012.

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Published In

cover image Communications of the ACM
Communications of the ACM  Volume 56, Issue 6
June 2013
104 pages
ISSN:0001-0782
EISSN:1557-7317
DOI:10.1145/2461256
Issue’s Table of Contents
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2013
Published in CACM Volume 56, Issue 6

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  • (2022)Knowledge‐Driven and Intelligent Computing in HealthcareHandbook of Intelligent Healthcare Analytics10.1002/9781119792550.ch8(167-188)Online publication date: 6-May-2022
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