Rank Aggregation and Prediction with Item Features
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:748-756, 2017.
We study the problem of rank aggregation with features, where both pairwise comparisons and item features are available to help the rank aggregation task. Observing that traditional rank aggregation methods disregard features, while models adapted from learning-to-rank task are sensitive to feature noise, we propose a general model to learn a total ranking by balancing between comparisons and feature information jointly. As a result, our proposed model takes advantage of item features and is also robust to noise. More importantly, we study the effectiveness of item features in our model and show that given sufficiently informative features, the sample complexity of our model can be asymptotically lower than models based only on comparisons for deriving an $ε$-accurate ranking. The results theoretically justify that our model can achieve efficient learning by leveraging item feature information. In addition, we show that the proposed model can also be extended to two other related problems—online rank aggregation and rank prediction of new items. Finally, experiments show that our model is more effective and robust compared to existing methods on both synthetic and real datasets.