Propagating Ranking Functions on a Graph: Algorithms and Applications


  • Buyue Qian IBM T. J. Watson Research
  • Xiang Wang IBM T. J. Watson Research
  • Ian Davidson University of California, Davis



Learning to Rank, Distribution Propagation, Linear Ranking function, Graph Diffusion, The Hellinger Distance


Learning to rank is an emerging learning task that opens up a diverse set of applications. However, most existing work focuses on learning a single ranking function whilst in many real world applications, there can be many ranking functions to fulfill various retrieval tasks on the same data set. How to train many ranking functions is challenging due to the limited availability of training data which is further compounded when plentiful training data is available for a small subset of the ranking functions. This is particularly true in settings, such as personalized ranking/retrieval, where each person requires a unique ranking function according to their preference, but only the functions of the persons who provide sufficient ratings (of objects, such as movies and music) can be well trained. To address this, we propose to construct a graph where each node corresponds to a retrieval task, and then propagate ranking functions on the graph. We illustrate the usefulness of the idea of propagating ranking functions and our method by exploring two real world applications.




How to Cite

Qian, B., Wang, X., & Davidson, I. (2015). Propagating Ranking Functions on a Graph: Algorithms and Applications. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



Main Track: Machine Learning Applications