Probabilistic latent semantic user segmentation for behavioral targeted advertising

ABSTRACT

Behavioral Targeting (BT), which aims to deliver the most appropriate advertisements to the most appropriate users, is attracting much attention in online advertising market. A key challenge of BT is how to automatically segment users for ads delivery, and good user segmentation may significantly improve the ad click-through rate (CTR). Different from classical user segmentation strategies, which rarely take the semantics of user behaviors into consideration, we propose in this paper a novel user segmentation algorithm named Probabilistic Latent Semantic User Segmentation (PLSUS). PLSUS adopts the probabilistic latent semantic analysis to mine the relationship between users and their behaviors so as to segment users in a semantic manner. We perform experiments on the real world ad click through log of a commercial search engine. Comparing with the other two classical clustering algorithms, K-Means and CLUTO, PLSUS can further improve the ads CTR up to 100%. To our best knowledge, this work is an early semantic user segmentation study for BT in academia.

REFERENCES

• 1: David M. Blei , Andrew Y. Ng , Michael I. Jordan, Latent dirichlet allocation, The Journal of Machine Learning Research, 3, p.993-1022, 3/1/2003
• 2: Thorsten Brants , Francine Chen , Ioannis Tsochantaridis, Topic-based document segmentation with probabilistic latent semantic analysis, Proceedings of the eleventh international conference on Information and knowledge management, November 04-09, 2002, McLean, Virginia, USA [doi>10.1145/584792.584829]
• 3: T. Brants and R. Stolle. Find similar documents in document collections. In Proceedings of LREC '02 (Spain, June 2002).
• 4: Andrei Broder , Marcus Fontoura , Vanja Josifovski , Lance Riedel, A semantic approach to contextual advertising, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277837]
• 5: David Cohn , Huan Chang, Learning to Probabilistically Identify Authoritative Documents, Proceedings of the Seventeenth International Conference on Machine Learning, p.167-174, June 29-July 02, 2000
• 6: D. Cohn and T. Hofmann. The missing link: A probabilistic model of document content and hypertext connectivity. In Proceeding of NIPS '00 (Denver, November 2000), MIT Press.
• 7: Abhinandan S. Das , Mayur Datar , Ashutosh Garg , Shyam Rajaram, Google news personalization: scalable online collaborative filtering, Proceedings of the 16th international conference on World Wide Web, May 08-12, 2007, Banff, Alberta, Canada [doi>10.1145/1242572.1242610]
• 8: S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Hashman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(1990), 391--407.
• 9: D. C. Fain and J. O. Pedersen. Sponsored search: a brief history. In Bulletin of the American Society for Information Science and Technology, 2005.
• 10: Éric Gaussier , Cyril Goutte , Kris Popat , Francine Chen, A Hierarchical Model for Clustering and Categorising Documents, Proceedings of the 24th BCS-IRSG European Colloquium on IR Research: Advances in Information Retrieval, p.229-247, March 25-27, 2002
• 11: Mark Girolami , Ata Kabán, On an equivalence between PLSI and LDA, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860537]
• 12: Abhay S. Harpale , Yiming Yang, Personalized active learning for collaborative filtering, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390352]
• 13: T. Hofmann. Probabilistic latent semantic analysis. In Proceedings of UAI '99 (Stockholm, July 1999), Morgan Kaufmann, 289--296.
• 14: Thomas Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.50-57, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312649]
• 15: Thomas Hofmann, Unsupervised learning by probabilistic latent semantic analysis, Machine Learning, v.42 n.1/2, p.177-196, Jan. 2001 [doi>10.1023/A:1007617005950]
• 16: Xin Jin , Yanzan Zhou , Bamshad Mobasher, Web usage mining based on probabilistic latent semantic analysis, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014076]
• 17: Y. Kim, J. Chang, and B. Zhang. An empirical study on dimensionality optimization in text mining for linguistic knowledge acquisition. In Proceedings of KDD '03 (Seoul, April 2003), ACM Press, 111--116.
• 18: Gerard Salton , Christopher Buckley, Term-weighting approaches in automatic text retrieval, Information Processing and Management: an International Journal, v.24 n.5, p.513-523, 1988 [doi>10.1016/0306-4573(88)90021-0]
• 19: Jun Yan , Ning Liu , Gang Wang , Wen Zhang , Yun Jiang , Zheng Chen, How much can behavioral targeting help online advertising?, Proceedings of the 18th international conference on World wide web, April 20-24, 2009, Madrid, Spain [doi>10.1145/1526709.1526745]
• 20: Adlink, https://www.google.com/adsense/login/en_US/?gsessionid= Dc28hZShnCI
• 21: Almond Net, http://www.almondnet.com/
• 22: Blue Lithium, http://www.bluelithium.com/
• 23: Burst, http://www.burstmedia.com/
• 24: Double Click, http://www.doubleclick.com/products/dfa/index.aspx
• 25: NebuAd, http://www.nebuad.com/
• 26: Phorm, http://www.phorm.com/
• 27: Revenue Science, http://www.revenuescience.com/advertisers/advertiser_solutions.asp
• 28: Specificmeida, http://www.specificmedia.co.uk/
• 29: TACODA, http://www.tacoda.com/
• 30: Yahoo! Smart Ads, http://advertising.yahoo.com/marketing/smartads/

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services
          Subjects: Commercial services

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.1 Models
          Subjects: Statistical

General Terms:
Algorithms, Experimentation, Performance

Keywords:
behavioral targeting (BT), probabilistic latent semantic analysis, user segmentation