Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model

• URL: http://arxiv.org/abs/2006.05067v3
• Date: Fri, 26 Feb 2021 00:58:44 GMT
• Title: Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
• Authors: Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei
• Abstract summary: Given $N$ items with $M$ partitions, calculating the likelihood of data with partitioned preference under the PL model has a time complexity of $O(N+S!)$. We propose an efficient numerical integration approach for calculating the likelihood and its gradients with a time complexity $O(N+S3)$.
• Score: 24.923231199480433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown. Given $N$ items with $M$ partitions, calculating the likelihood of data with partitioned preference under the PL model has a time complexity of $O(N+S!)$, where $S$ is the maximum size of the top $M-1$ partitions. This computational challenge restrains most existing PL-based listwise LTR methods to a special case of partitioned preference, top-$K$ ranking, where the exact order of the top $K$ items is known. In this paper, we exploit a random utility model formulation of the PL model, and propose an efficient numerical integration approach for calculating the likelihood and its gradients with a time complexity $O(N+S^3)$. We demonstrate that the proposed method outperforms well-known LTR baselines and remains scalable through both simulation experiments and applications to real-world eXtreme Multi-Label classification tasks.

Related papers

• Computational-Statistical Gaps in Gaussian Single-Index Models [77.1473134227844]
  Single-Index Models are high-dimensional regression problems with planted structure. We show that computationally efficient algorithms, both within the Statistical Query (SQ) and the Low-Degree Polynomial (LDP) framework, necessarily require $Omega(dkstar/2)$ samples.
  arXiv  Detail & Related papers  (2024-03-08T18:50:19Z)
• Ranking Inferences Based on the Top Choice of Multiway Comparisons [2.468314282946207]
  This paper considers ranking inference of $n$ items based on the observed data on the top choice among $M$ randomly selected items at each trial. It is a useful modification of the Plackett-Luce model for $M$-way ranking with only the top choice observed and is an extension of the celebrated Bradley-Terry-Luce model that corresponds to $M=2$.
  arXiv  Detail & Related papers  (2022-11-22T02:34:52Z)
• You can't pick your neighbors, or can you? When and how to rely on retrieval in the $k$NN-LM [65.74934004876914]
  Retrieval-enhanced language models (LMs) condition their predictions on text retrieved from large external datastores. One such approach, the $k$NN-LM, interpolates any existing LM's predictions with the output of a $k$-nearest neighbors model. We empirically measure the effectiveness of our approach on two English language modeling datasets.
  arXiv  Detail & Related papers  (2022-10-28T02:57:40Z)
• Efficient and Accurate Top-$K$ Recovery from Choice Data [1.14219428942199]
  In some applications such as recommendation systems, the statistician is primarily interested in recovering the set of the top ranked items from a large pool of items. We propose the choice-based Borda count algorithm as a fast and accurate ranking algorithm for top $K$-recovery. We show that the choice-based Borda count algorithm has optimal sample complexity for top-$K$ recovery under a broad class of random utility models.
  arXiv  Detail & Related papers  (2022-06-23T22:05:08Z)
• Matching Pursuit Based Scheduling for Over-the-Air Federated Learning [67.59503935237676]
  This paper develops a class of low-complexity device scheduling algorithms for over-the-air learning via the method of federated learning. Compared to the state-of-the-art proposed scheme, the proposed scheme poses a drastically lower efficiency system. The efficiency of the proposed scheme is confirmed via experiments on the CIFAR dataset.
  arXiv  Detail & Related papers  (2022-06-14T08:14:14Z)
• The Power of Subsampling in Submodular Maximization [51.629656762796564]
  We show that this approach leads to optimal/state-of-the-art results despite being much simpler than existing methods. We empirically demonstrate the effectiveness of our algorithms on video summarization, location summarization, and movie recommendation tasks.
  arXiv  Detail & Related papers  (2021-04-06T20:25:57Z)
• Sampling from a $k$-DPP without looking at all items [58.30573872035083]
  Given a kernel function and a subset size $k$, our goal is to sample $k$ out of $n$ items with probability proportional to the determinant of the kernel matrix induced by the subset (a.k.a. $k$-DPP) Existing $k$-DPP sampling algorithms require an expensive preprocessing step which involves multiple passes over all $n$ items, making it infeasible for large datasets. We develop an algorithm which adaptively builds a sufficiently large uniform sample of data that is then used to efficiently generate a smaller set of $k$ items.
  arXiv  Detail & Related papers  (2020-06-30T16:40:44Z)
• SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm [64.13217062232874]
  SURF is an algorithm for approximating distributions by piecewises. It outperforms state-of-the-art algorithms in experiments.
  arXiv  Detail & Related papers  (2020-02-22T01:03:33Z)
• Best-item Learning in Random Utility Models with Subset Choices [40.17224226373741]
  We consider the problem of PAC learning the most valuable item from a pool of $n$ items using sequential, adaptively chosen plays of subsets of $k$ items. We identify a new property of such a RUM, termed the minimum advantage, that helps in characterizing the complexity of separating pairs of items. We give a learning algorithm for general RUMs, based on pairwise relative counts of items and hierarchical elimination, along with a new PAC sample complexity guarantee.
  arXiv  Detail & Related papers  (2020-02-19T03:57:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.