Exploiting content relevance and social relevance for personalized ad recommendation on internet TV

  • Authors:

  • Bo Wang;Jinqiao Wang;Hanqing Lu

  • Affiliations:

  • Communication University of China, Chinese Academy of Science, Beijing, China;Communication University of China, Beijing, China;Chinese Academy of Science, Beijing, China

  • Venue:
  • ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
  • Year:
  • 2013

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Abstract

There have been not many interactions between the two dominant forms of mass communication: television and the Internet, while nowadays the appearance of Internet television makes them more closely. Different with traditional TV in a passive mode of transmission, Internet TV makes it more possible to make personalized service recommendation because of the interactivity between users and the Internet. In this article, we introduce a scheme to provide targeted ad recommendation to Internet TV users by exploiting the content relevance and social relevance. First, we annotate TV videos in terms of visual content analysis and textual analysis by aligning visual and textual information. Second, with user-user, video-video and user-video relationships, we employ Multi-Relationship based Probabilistic Matrix Factorization (MRPMF) to learn representative tags for modeling user preference. And then semantic content relevance (between product/ad and TV video) and social relevance (between product/ad and user interest) are calculated by projecting the corresponding tags into our advertising concept space. Finally, with relevancy scores we make ranking for relevant product/ads to effectively provide users personalized recommendation. The experimental results demonstrate attractiveness and effectiveness of our proposed approach.