Unbiased Pairwise Learning to Rank in Recommender Systems

• URL: http://arxiv.org/abs/2111.12929v1
• Date: Thu, 25 Nov 2021 06:04:59 GMT
• Title: Unbiased Pairwise Learning to Rank in Recommender Systems
• Authors: Yi Ren, Hongyan Tang and Siwen Zhu
• Abstract summary: Unbiased learning to rank algorithms are appealing candidates and have already been applied in many applications with single categorical labels. We propose a novel unbiased LTR algorithm to tackle the challenges, which innovatively models position bias in the pairwise fashion. Experiment results on public benchmark datasets and internal live traffic show the superior results of the proposed method for both categorical and continuous labels.
• Score: 4.058828240864671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, recommender systems already impact almost every facet of peoples lives. To provide personalized high quality recommendation results, conventional systems usually train pointwise rankers to predict the absolute value of objectives and leverage a distinct shallow tower to estimate and alleviate the impact of position bias. However, with such a training paradigm, the optimization target differs a lot from the ranking metrics valuing the relative order of top ranked items rather than the prediction precision of each item. Moreover, as the existing system tends to recommend more relevant items at higher positions, it is difficult for the shallow tower based methods to precisely attribute the user feedback to the impact of position or relevance. Therefore, there exists an exciting opportunity for us to get enhanced performance if we manage to solve the aforementioned issues. Unbiased learning to rank algorithms, which are verified to model the relative relevance accurately based on noisy feedback, are appealing candidates and have already been applied in many applications with single categorical labels, such as user click signals. Nevertheless, the existing unbiased LTR methods cannot properly handle multiple feedback incorporating both categorical and continuous labels. Accordingly, we design a novel unbiased LTR algorithm to tackle the challenges, which innovatively models position bias in the pairwise fashion and introduces the pairwise trust bias to separate the position bias, trust bias, and user relevance explicitly. Experiment results on public benchmark datasets and internal live traffic show the superior results of the proposed method for both categorical and continuous labels.

Related papers

• Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages [55.04219793298687]
  This paper shows how efficiently-solvable fair ranking models can be integrated into the training loop of Learning to Rank. In particular, this paper is the first to show how to backpropagate through constrained optimizations of OWA objectives, enabling their use in integrated prediction and decision models.
  arXiv  Detail & Related papers  (2024-02-07T20:53:53Z)
• Metrics for popularity bias in dynamic recommender systems [0.0]
  Biased recommendations may lead to decisions that can potentially have adverse effects on individuals, sensitive user groups, and society. This paper focuses on quantifying popularity bias that stems directly from the output of RecSys models. Four metrics to quantify popularity bias in RescSys over time in dynamic setting across different sensitive user groups have been proposed.
  arXiv  Detail & Related papers  (2023-10-12T16:15:30Z)
• Unbiased Learning to Rank with Biased Continuous Feedback [5.561943356123711]
  Unbiased learning-to-rank(LTR) algorithms are verified to model the relative relevance accurately based on noisy feedback. To provide personalized high-quality recommendation results, recommender systems need model both categorical and continuous biased feedback. We introduce the pairwise trust bias to separate the position bias, trust bias, and user relevance explicitly. Experiment results on public benchmark datasets and internal live traffic of a large-scale recommender system at Tencent News show superior results for continuous labels.
  arXiv  Detail & Related papers  (2023-03-08T02:14:08Z)
• Managing multi-facet bias in collaborative filtering recommender systems [0.0]
  Biased recommendations across groups of items can endanger the interests of item providers along with causing user dissatisfaction with the system. This study aims to manage a new type of intersectional bias regarding the geographical origin and popularity of items in the output of state-of-the-art collaborative filtering recommender algorithms. Extensive experiments on two real-world datasets of movies and books, enriched with the items' continents of production, show that the proposed algorithm strikes a reasonable balance between accuracy and both types of the mentioned biases.
  arXiv  Detail & Related papers  (2023-02-21T10:06:01Z)
• Whole Page Unbiased Learning to Rank [59.52040055543542]
  Unbiased Learning to Rank(ULTR) algorithms are proposed to learn an unbiased ranking model with biased click data. We propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, named BAL, to automatically find the user behavior model. Experimental results on a real-world dataset verify the effectiveness of the BAL.
  arXiv  Detail & Related papers  (2022-10-19T16:53:08Z)
• Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
  We show how to return a set of items rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate. We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
  arXiv  Detail & Related papers  (2022-07-04T17:49:25Z)
• Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
  We develop a new learning paradigm named Cross Pairwise Ranking (CPR) CPR achieves unbiased recommendation without knowing the exposure mechanism. We prove in theory that this way offsets the influence of user/item propensity on the learning.
  arXiv  Detail & Related papers  (2022-04-26T09:20:27Z)
• Correcting the User Feedback-Loop Bias for Recommendation Systems [34.44834423714441]
  We propose a systematic and dynamic way to correct user feedback-loop bias in recommendation systems. Our method includes a deep-learning component to learn each user's dynamic rating history embedding. We empirically validated the existence of such user feedback-loop bias in real world recommendation systems.
  arXiv  Detail & Related papers  (2021-09-13T15:02:55Z)
• Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility [54.179859639868646]
  Bipartite ranking aims to learn a scoring function that ranks positive individuals higher than negative ones from labeled data. There have been rising concerns on whether the learned scoring function can cause systematic disparity across different protected groups. We propose a model post-processing framework for balancing them in the bipartite ranking scenario.
  arXiv  Detail & Related papers  (2020-06-15T10:08:39Z)
• SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback [50.13745601531148]
  We propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons. We also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to $sqrtM/N$.
  arXiv  Detail & Related papers  (2020-02-23T06:40:48Z)

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.