Probabilistic latent semantic user segmentation for behavioral targeted advertising

  • Authors:

  • Xiaohui Wu;Jun Yan;Ning Liu;Shuicheng Yan;Ying Chen;Zheng Chen

  • Affiliations:

  • Beijing Institute of Technology, Beijing, China and Microsoft Research Asia, Sigma Center, Beijing, China;Microsoft Research Asia, Sigma Center, Beijing, China;Microsoft Research Asia, Sigma Center, Beijing, China;National University of Singapore, Singapore;Beijing Institute of Technology, Beijing, China;Microsoft Research Asia, Sigma Center, Beijing, China

  • Venue:
  • Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
  • Year:
  • 2009

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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.