Aligning Recommendations with User Popularity Preferences

• URL: http://arxiv.org/abs/2604.01036v1
• Date: Wed, 01 Apr 2026 15:45:24 GMT
• Title: Aligning Recommendations with User Popularity Preferences
• Abstract summary: Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items.<n>This work studies popularity bias through the lens of user-recommender alignment.<n>We propose SPREE, an inference-time mitigation method for sequential recommenders based on activation steering.
• Score: 2.1976433151201658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in "rich-get-richer" dynamics and a homogenization of visible content, but can also lead to misalignment of recommendations with individual users' preferences for popular or niche content. This work studies popularity bias through the lens of user-recommender alignment. To this end, we introduce Popularity Quantile Calibration, a measurement framework that quantifies misalignment between a user's historical popularity preference and the popularity of their recommendations. Building on this notion of popularity alignment, we propose SPREE, an inference-time mitigation method for sequential recommenders based on activation steering. SPREE identifies a popularity direction in representation space and adaptively steers model activations based on an estimate of each user's personal popularity bias, allowing both the direction and magnitude of steering to vary across users. Unlike global debiasing approaches, SPREE explicitly targets alignment rather than uniformly reducing popularity. Experiments across multiple datasets show that SPREE consistently improves user-level popularity alignment while preserving recommendation quality.

Related papers

• Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders [65.5347571901244]
  This study focuses on the optimization of generative framework and the item tokenization based on semantic index.<n>Based on theoretical analyses, we identify that the severe popularity bias emerges from the confluence of a token-level optimization flaw and the undifferentiated property of item tokenization.<n>This study develops a novel generative recommender system, called Ghost, by designing the asymmetric unlikelihood optimization and the skeleton-founded tokenization.
  arXiv  Detail & Related papers  (2026-05-16T06:02:40Z)
• Tree of Preferences for Diversified Recommendation [54.183647833064136]
  We study diversified recommendation from a data-bias perspective.<n>Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach.
  arXiv  Detail & Related papers  (2025-12-24T04:13:17Z)
• Balancing Accuracy and Novelty with Sub-Item Popularity [54.56622169534604]
  We propose a novel integration of sub-ID-level personalised popularity within the RecJPQ framework.<n>Our method consistently outperforms item-level PPS by achieving significantly higher personalised novelty without compromising recommendation accuracy.
  arXiv  Detail & Related papers  (2025-08-07T09:33:32Z)
• PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems [1.0128808054306186]
  We propose PBiLoss, a regularization-based loss function designed to counteract popularity bias in graph-based recommender models explicitly.<n>We show that PBiLoss significantly improves fairness, as demonstrated by reductions in the Popularity-Rank Correlation for Users (PRU) and Popularity-Rank Correlation for Items (PRI)
  arXiv  Detail & Related papers  (2025-07-25T08:29:32Z)
• Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations [1.389360509566256]
  Point-of-interest (POI) recommender systems help users discover relevant locations, but their effectiveness is often compromised by popularity bias.<n>This paper addresses this challenge by evaluating the effectiveness of context-aware models and calibrated popularity techniques as strategies for mitigating popularity bias.
  arXiv  Detail & Related papers  (2025-07-04T11:56:11Z)
• Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity [21.645389873171908]
  Popularity bias occurs when popular items are recommended far more frequently than they should be.<n>Existing debiasing methods mitigate popularity bias often uniformly across all users.<n>We propose a novel method called CausalEPP (Causal Intervention on Evolving Personal Popularity) for taming recommendation bias.
  arXiv  Detail & Related papers  (2025-05-20T12:59:16Z)
• Towards Popularity-Aware Recommendation: A Multi-Behavior Enhanced Framework with Orthogonality Constraint [4.137753517504481]
  Top-$K$ recommendation involves inferring latent user preferences and generating personalized recommendations.<n>We present a textbfPopularity-aware top-$K$ recommendation algorithm integrating multi-behavior textbfSide textbfInformation.
  arXiv  Detail & Related papers  (2024-12-26T11:06:49Z)
• Preference Diffusion for Recommendation [50.8692409346126]
  We propose PreferDiff, a tailored optimization objective for DM-based recommenders.<n> PreferDiff transforms BPR into a log-likelihood ranking objective to better capture user preferences.<n>It is the first personalized ranking loss designed specifically for DM-based recommenders.
  arXiv  Detail & Related papers  (2024-10-17T01:02:04Z)
• Enhancing Sequential Music Recommendation with Personalized Popularity Awareness [56.972624411205224]
  This paper introduces a novel approach that incorporates personalized popularity information into sequential recommendation. Experimental results demonstrate that a Personalized Most Popular recommender outperforms existing state-of-the-art models.
  arXiv  Detail & Related papers  (2024-09-06T15:05:12Z)
• 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)
• 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)

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.