Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
The Journal of Machine Learning Research
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Top-k learning to rank: labeling, ranking and evaluation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Is top-k sufficient for ranking?
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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This paper is concerned with top-k ranking problem, which reflects the fact that people pay more attention to the top ranked objects in real ranking application like information retrieval. A popular approach to top-k ranking problem is based on probabilistic models, such as Luce model and Mallows model. However, whether the sequential generative process described in these models is a suitable way for top-k ranking remains a question. According to the riffled independence factorization proposed in recent literature, which is a natural structural assumption on top-k ranking, we propose a new generative process of top-k ranking data. Our approach decomposes distributions over the top-k ranking into two layers: the first layer describes the relative ordering between the top k objects and the rest n-k objects, and the second layer describes the full ordering on the top k objects. On this basis, we propose a new probabilistic model for top-k ranking problem, called hierarchical ordering model. Specifically, we use three different probabilistic models to describe different generative processes of the first layer, and Luce model to describe the sequential generative process of the second layer, thus we obtain three different specific hierarchical ordering models. We also conduct extensive experiments on benchmark datasets to show that our proposed models can outperform previous models significantly.