skip to main content
10.1145/2740908.2741997acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

Serving Ads to "Yahoo Answers" Occasional Visitors

Published: 18 May 2015 Publication History

Abstract

Modern ad serving systems can benefit when allowed to accumulate user information and use it as part of the serving algorithm. However, this often does not coincide with how the web is used. Many domains will see users for only brief interactions, as users enter a domain through a search result or social media link and then leave. Having access to little or no user information and no ability to assemble a user profile over a prolonged period of use, we would still like to leverage the information we have to the best of our ability. In this paper we attempt several methods of improving ad serving for occasional users, including leveraging user information that is still available, content analysis of the page, information about the page's content generators and historical breakdown of visits to the page. We compare and combine these methods in a framework of a collaborative filtering algorithm, test them on real data collected from Yahoo Answers, and achieve significant improvements over baseline algorithms.

References

[1]
D. Agarwal and B.-C. Chen. Regression-based latent factor models. In Proc. SIGKDD'2009, pages 19--28. ACM, 2009.
[2]
M. Aharon, N. Aizenberg, E. Bortnikov, R. Lempel, R. Adadi, T. Benyamini, L. Levin, R. Roth, and O. Serfaty. OFF-set: one-pass factorization of feature sets for online recommendation in persistent cold start settings. In Proc. RecSys'2013.
[3]
M. Aharon, A. Kagian, Y. Koren, and R. Lempel. Dynamic personalized recommendation of comment-eliciting stories. In Proc. of RecSys'2012.
[4]
N. Aizenberg, Y. Koren, and O. Somekh. Build your own music recommender by modeling internet radio streams. In Proc. WWW'2012.
[5]
D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.
[6]
O. Chapelle and L. Li. An empirical evaluation of Thompson sampling. In Proc. NIPS'2011.
[7]
G. Dror, N. Koenigstein, and Y. Koren. Yahoo! music recommendations: Modeling music ratings with temporal dynamics and item. In Proc. RecSys'2011.
[8]
B. Edelman, M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. Technical report, National Bureau of Economic Research, 2005.
[9]
T. Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 27(8):861--874, June 2006.
[10]
N. Golbandi, Y. Koren, and R. Lempel. Adaptive bootstrapping of recommender systems using decision trees. In Proc. WSDM'2011.
[11]
N. Golbandi, Y. Koren, and R. Lempel. On bootstrapping recommender systems. In Proc. CIKM'2010.
[12]
A. Gunawardana and C. Meek. Tied Boltzmann machines for cold start recommendations. In Proc. RecSys'2008.
[13]
A. Gunawardana and C. Meek. A unified approach to building hybrid recommender systems. In Proc. RecSys'2009.
[14]
X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers, et al. Practical lessons from predicting clicks on ads at facebook. In Proc. SIGKDD'2014.
[15]
A. Kohrs and B. Merialdo. Improving collaborative filtering for new users by smart object selection. In Proc. ICMF'2001.
[16]
Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proc. KDD'2008.
[17]
Y. Koren. Collaborative filtering with temporal dynamics. Communications of the ACM, 53(4):89--97, 2010.
[18]
Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009.
[19]
S.-L. Lee. Commodity recommendations of retail business based on decision tree induction. Expert Systems with Applications, 37(5):3685--3694, 2010.
[20]
G. Linden, B. Smith, and J. York. Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1):76--80, 2003.
[21]
H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, et al. Ad click prediction: a view from the trenches. In Proc. SIGKDD'2013.
[22]
S.-T. Park and W. Chu. Pairwise preference regression for cold-start recommendation. In Proc. RecSys'2009.
[23]
A. Paterek. Improving regularized singular value decomposition for collaborative filtering. In Proc. KDD Cup 2007.
[24]
A. M. Rashid, I. Albert, D. Cosley, S. K. Lam, S. M. McNee, J. A. Konstan, and J. Riedl. Getting to know you: learning new user preferences in recommender systems. In Proc. International Conference on Intelligent User Interfaces, 2002.
[25]
A. M. Rashid, G. Karypis, and J. Riedl. Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explorations Newsletter, 10(2):90--100, 2008.
[26]
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. WWW'2001.
[27]
I. Szpektor, Y. Maarek, and D. Pelleg. When relevance is not enough: promoting diversity and freshness in personalized question recommendation. In Proc. WWW'2013.
[28]
K. Zhou, S.-H. Yang, and H. Zha. Functional matrix factorizations for cold-start recommendation. In Proc. SIGIR'2011.

Cited By

View all
  • (2022)Multi-Armed Bandits in Recommendation SystemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116669197:COnline publication date: 18-May-2022
  • (2016)Analyzing and predicting knowledge of contributors in community question answering services2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)10.1109/WiSPNET.2016.7566588(2473-2477)Online publication date: Mar-2016

Index Terms

  1. Serving Ads to "Yahoo Answers" Occasional Visitors

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
    May 2015
    1602 pages
    ISBN:9781450334730
    DOI:10.1145/2740908

    Sponsors

    • IW3C2: International World Wide Web Conference Committee

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 May 2015

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Yahoo answers
    2. collaborative filtering
    3. user cold-start problem

    Qualifiers

    • Research-article

    Conference

    WWW '15
    Sponsor:
    • IW3C2

    Acceptance Rates

    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 15 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Multi-Armed Bandits in Recommendation SystemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116669197:COnline publication date: 18-May-2022
    • (2016)Analyzing and predicting knowledge of contributors in community question answering services2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)10.1109/WiSPNET.2016.7566588(2473-2477)Online publication date: Mar-2016

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media