Zeroshot Listwise Learning to Rank Algorithm for Recommendation

• URL: http://arxiv.org/abs/2409.13703v1
• Date: Thu, 05 Sep 2024 09:16:14 GMT
• Title: Zeroshot Listwise Learning to Rank Algorithm for Recommendation
• Authors: Hao Wang,
• Abstract summary: Learning to rank is a rare technology compared with other techniques such as deep neural networks. We design a zeroshot listwise learning to rank algorithm for recommendation.
• Score: 5.694872363688119
• License:
• Abstract: Learning to rank is a rare technology compared with other techniques such as deep neural networks. The number of experts in the field is roughly 1/6 of the number of professionals in deep learning. Being an effective ranking methodology, learning to rank has been widely used in the field of information retrieval. However, in recent years, learning to rank as a recommendation approach has been on decline. In this paper, we take full advantage of order statistic approximation and power law distribution to design a zeroshot listwise learning to rank algorithm for recommendation. We prove in the experiment section that our approach is both accurate and fair.

Related papers

• Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers [56.12593882838412]
  We introduce a novel instruction distillation method to rank documents. We first rank documents using the effective pairwise approach with complex instructions, and then distill the teacher predictions to the pointwise approach with simpler instructions. Our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods.
  arXiv  Detail & Related papers  (2023-11-02T19:16:21Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems [4.658166900129066]
  We show that our algorithm is competitive with other algorithms when evaluated on technical accuracy metrics. What is more important, in our experiment section we demonstrate that Pareto Pairwise Ranking is the most fair algorithm in comparison with 9 other contemporary algorithms.
  arXiv  Detail & Related papers  (2022-12-06T03:46:31Z)
• Which Tricks Are Important for Learning to Rank? [32.38701971636441]
  State-of-the-art learning-to-rank methods are based on gradient-boosted decision trees (GBDT) In this paper, we thoroughly analyze several GBDT-based ranking algorithms in a unified setup. As a result, we gain insights into learning-to-rank techniques and obtain a new state-of-the-art algorithm.
  arXiv  Detail & Related papers  (2022-04-04T13:59:04Z)
• Optimized Recommender Systems with Deep Reinforcement Learning [0.0]
  This work investigates and develops means to setup a reproducible testbed, and evaluate different state of the art algorithms in a realistic environment. It entails a proposal, literature review, methodology, results, and comments.
  arXiv  Detail & Related papers  (2021-10-06T19:54:55Z)
• The Information Geometry of Unsupervised Reinforcement Learning [133.20816939521941]
  Unsupervised skill discovery is a class of algorithms that learn a set of policies without access to a reward function. We show that unsupervised skill discovery algorithms do not learn skills that are optimal for every possible reward function.
  arXiv  Detail & Related papers  (2021-10-06T13:08:36Z)
• PiRank: Learning To Rank via Differentiable Sorting [85.28916333414145]
  We propose PiRank, a new class of differentiable surrogates for ranking. We show that PiRank exactly recovers the desired metrics in the limit of zero temperature.
  arXiv  Detail & Related papers  (2020-12-12T05:07:36Z)
• Mastering Rate based Curriculum Learning [78.45222238426246]
  We argue that the notion of learning progress itself has several shortcomings that lead to a low sample efficiency for the learner. We propose a new algorithm, based on the notion of mastering rate, that significantly outperforms learning progress-based algorithms.
  arXiv  Detail & Related papers  (2020-08-14T16:34:01Z)
• Controlling Fairness and Bias in Dynamic Learning-to-Rank [31.41843594914603]
  We propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.
  arXiv  Detail & Related papers  (2020-05-29T17:57:56Z)
• Meta-learning with Stochastic Linear Bandits [120.43000970418939]
  We consider a class of bandit algorithms that implement a regularized version of the well-known OFUL algorithm, where the regularization is a square euclidean distance to a bias vector. We show both theoretically and experimentally, that when the number of tasks grows and the variance of the task-distribution is small, our strategies have a significant advantage over learning the tasks in isolation.
  arXiv  Detail & Related papers  (2020-05-18T08:41:39Z)
• Listwise Learning to Rank with Deep Q-Networks [3.9726605190181976]
  We show that DeepQRank, our deep q-learning to rank agent, demonstrates performance that can be considered state-of-the-art. We run our algorithm against Microsoft's LETOR listwise dataset and achieve an NDCG@1 of 0.5075, narrowly beating out the leading supervised learning model, SVMRank (0.4958)
  arXiv  Detail & Related papers  (2020-02-13T22:45:56Z)

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.