Co-Factorization Machines: Modeling User Interests and Predicting Individual Decisions in Twitter

Liangjie Hong, Aziz S. Doumith and Brian D. Davison

One of eight finalists for best paper award.
Full Paper (10 pages)
Official ACM published version:
Author's version: PDF (263KB)
NOTE: The published version of this paper has font problems resulting in missing characters from the figures. The author version of this paper has corrected that error.

Users of popular services like Twitter and Facebook are often simultaneously overwhelmed with the amount of information delivered via their social connections and miss out on much content that they might have liked to see, even though it was distributed outside of their social circle. Both issues serve as difficulties to the users and drawbacks to the services.

Social media service providers can benefit from understanding user interests and how they interact with the service, potentially predicting their behaviors in the future. In this paper, we address the problem of simultaneously predicting user decisions and modeling users' interests in social media by analyzing rich information gathered from Twitter. The task differs from conventional recommender systems as the cold-start problem is ubiquitous, and rich features, including textual content, need to be considered. We build predictive models for user decisions in Twitter by proposing Co-Factorization Machines (CoFM), an extension of a state-of-the-art recommendation model, to handle multiple aspects of the dataset at the same time. Additionally, we discuss and compare ranking-based loss functions in the context of recommender systems, providing the first view of how they vary from each other and perform in real tasks. We explore an extensive set of features and conduct experiments on a real-world dataset, concluding that CoFM with ranking-based loss functions is superior to state-of-the-art methods and yields interpretable latent factors.

In Proceedings of the 6th Annual ACM International Conference on Web Search and Data Mining, pages 557-566, Rome, Italy, February 2013.

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Last modified: 13 March 2013
Brian D. Davison