An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System

• URL: http://arxiv.org/abs/2109.05677v1
• Date: Mon, 13 Sep 2021 03:04:55 GMT
• Title: An Adaptive Boosting Technique to Mitigate Popularity Bias in Recommender System
• Authors: Ajay Gangwar and Shweta Jain
• Abstract summary: A typical accuracy measure is biased towards popular items, i.e., it promotes better accuracy for popular items compared to non-popular items. This paper considers a metric that measures the popularity bias as the difference in error on popular items and non-popular items. Motivated by the fair boosting algorithm on classification, we propose an algorithm that reduces the popularity bias present in the data.
• Score: 1.5800354337004194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The observed ratings in most recommender systems are subjected to popularity bias and are thus not randomly missing. Due to this, only a few popular items are recommended, and a vast number of non-popular items are hardly recommended. Not suggesting the non-popular items lead to fewer products dominating the market and thus offering fewer opportunities for creativity and innovation. In the literature, several fair algorithms have been proposed which mainly focused on improving the accuracy of the recommendation system. However, a typical accuracy measure is biased towards popular items, i.e., it promotes better accuracy for popular items compared to non-popular items. This paper considers a metric that measures the popularity bias as the difference in error on popular items and non-popular items. Motivated by the fair boosting algorithm on classification, we propose an algorithm that reduces the popularity bias present in the data while maintaining accuracy within acceptable limits. The main idea of our algorithm is that it lifts the weights of the non-popular items, which are generally underrepresented in the data. With the help of comprehensive experiments on real-world datasets, we show that our proposed algorithm outperforms the existing algorithms on the proposed popularity bias metric.

Related papers

• Large Language Models as Recommender Systems: A Study of Popularity Bias [46.17953988777199]
  Popular items are disproportionately recommended, overshadowing less popular but potentially relevant items. Recent advancements have seen the integration of general-purpose Large Language Models into recommender systems. Our study explores whether LLMs contribute to or can alleviate popularity bias in recommender systems.
  arXiv  Detail & Related papers  (2024-06-03T12:53:37Z)
• Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias [34.006766098392525]
  Collaborative Filtering (CF) typically suffers from the challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to a significant accuracy gap between popular and unpopular items. We propose Popularity-Aware Alignment and Contrast (PAAC) to address two challenges.
  arXiv  Detail & Related papers  (2024-05-31T09:14:48Z)
• Test Time Embedding Normalization for Popularity Bias Mitigation [6.145760252113906]
  Popularity bias is a widespread problem in the field of recommender systems. We propose 'Test Time Embedding Normalization' as a simple yet effective strategy for mitigating popularity bias.
  arXiv  Detail & Related papers  (2023-08-22T08:57:44Z)
• A Survey on Popularity Bias in Recommender Systems [5.952279576277445]
  We discuss the potential reasons for popularity bias and review existing approaches to detect, mitigate and quantify popularity bias in recommender systems. We critically discuss todays literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.
  arXiv  Detail & Related papers  (2023-08-02T12:58:11Z)
• 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)
• Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets [62.146882023375746]
  We propose a model of an idealized online cultural market in which $N$ items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items' popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings.
  arXiv  Detail & Related papers  (2022-04-28T14:36:11Z)
• The Unfairness of Popularity Bias in Book Recommendation [0.0]
  Popularity bias refers to the problem that popular items are recommended frequently while less popular items are recommended rarely or not at all. We analyze the well-known Book-Crossing dataset and define three user groups based on their tendency towards popular items. Our results indicate that most state-of-the-art recommendation algorithms suffer from popularity bias in the book domain.
  arXiv  Detail & Related papers  (2022-02-27T20:21:46Z)
• Information-Theoretic Bias Reduction via Causal View of Spurious Correlation [71.9123886505321]
  We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation. We present a novel debiasing framework against the algorithmic bias, which incorporates a bias regularization loss. The proposed bias measurement and debiasing approaches are validated in diverse realistic scenarios.
  arXiv  Detail & Related papers  (2022-01-10T01:19:31Z)
• User-centered Evaluation of Popularity Bias in Recommender Systems [4.30484058393522]
  Recommendation and ranking systems suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. In this paper, we show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users' perspective. We present an effective approach that mitigates popularity bias from the user-centered point of view.
  arXiv  Detail & Related papers  (2021-03-10T22:12:51Z)
• 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.