Reducing Popularity Bias in Recommendation Over Time

Himan Abdollahpouri; Robin Burke

arxiv: 1906.11711 · v1 · pith:QL25Q3A6new · submitted 2019-06-27 · 💻 cs.IR

Reducing Popularity Bias in Recommendation Over Time

Himan Abdollahpouri , Robin Burke This is my paper

Pith reviewed 2026-05-25 14:23 UTC · model grok-4.3

classification 💻 cs.IR

keywords popularity biasrecommendation systemslong-tail coveragediversificationtemporal aspectsxQuAD algorithm

0 comments

The pith

A temporal version of xQuAD reduces popularity bias by periodically compensating for past long-tail omissions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that popularity bias, in which a few popular items dominate recommendations while long-tail items receive little exposure, can be addressed through a time-sensitive strategy rather than one-time adjustments. The algorithm evaluates its long-tail coverage at regular intervals and adjusts present recommendations to offset earlier shortfalls. This matters because bias can accumulate across repeated recommendation cycles, limiting item discovery over extended periods. A sympathetic reader would care if the approach delivers measurable gains in coverage while preserving accuracy levels.

Core claim

The central claim is that a temporal adaptation of the xQuAD diversification algorithm, which assesses long-tail coverage at regular intervals and compensates in the present for omissions in the past, achieves a superior tradeoff between long-tail coverage and accuracy compared to other existing approaches, as shown in experiments on two public datasets.

What carries the argument

Temporal xQuAD, the diversification algorithm that performs periodic long-tail coverage assessment and compensation for prior shortfalls.

If this is right

Long-tail coverage improves across repeated recommendation cycles compared with static diversification methods.
The accuracy-coverage tradeoff is better than that of several existing approaches on the tested datasets.
Bias that accumulates over time receives ongoing correction rather than a single fix.
The method provides a concrete mechanism for tracking and addressing exposure imbalances in sequential recommendations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The periodic compensation idea could extend to other time-dependent biases such as position or recency effects.
Systems might need to maintain historical exposure logs to implement the compensation step effectively.
Over many cycles the approach could increase overall catalog discovery for users without explicit diversity goals.
The method assumes stable item popularity trends; sudden shifts in item appeal might require additional adjustments.

Load-bearing premise

Periodic assessment of long-tail coverage and compensation for past omissions can be performed without degrading user satisfaction or requiring knowledge of how user preferences evolve.

What would settle it

Run the temporal method and baseline algorithms on the two public datasets over multiple successive time steps, then measure whether long-tail item exposure rates increase while accuracy metrics remain at or above baseline levels.

Figures

Figures reproduced from arXiv: 1906.11711 by Himan Abdollahpouri, Robin Burke.

**Figure 2.** Figure 2: The epoch-wise ARP and cumulative LCR (CLCR) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

read the original abstract

Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure. Research in this area so far has concentrated on a one-shot representation of this bias, and on algorithms to improve the diversity of individual recommendation lists. In this work, we take a time-sensitive view of popularity bias, in which the algorithm assesses its long-tail coverage at regular intervals, and compensates in the present moment for omissions in the past. In particular, we present a temporal version of the well-known xQuAD diversification algorithm adapted for long-tail recommendation. Experimental results on two public datasets show that our method is more effective in terms of the long-tail coverage and accuracy tradeoff compared to some other existing approaches.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Temporal adaptation of xQuAD gives a workable way to track and fix long-tail omissions over time on two datasets, but stays incremental.

read the letter

The main takeaway is that this paper turns xQuAD into a running process: it checks long-tail coverage at set intervals and re-ranks to make up for items that were under-exposed earlier. That time-sensitive framing is the actual addition over the one-shot bias papers it cites. The experiments on two public datasets report a better coverage-accuracy balance than some existing approaches, which is a concrete, testable result rather than just a new framing. The method itself is a direct adaptation, so the implementation details are straightforward to follow from the original xQuAD work. That keeps the contribution focused and reproducible in principle. The central claim does not collapse into circularity or hidden fitting; the periodic compensation is defined on historical data and evaluated on standard metrics. The paper engages honestly with the prior literature on popularity bias and diversification without overstating the leap. The soft spot is that the work remains an extension rather than a new mechanism, and the abstract gives limited visibility into the exact re-ranking schedule, metric definitions, or full baseline set. Without those, it is hard to judge how large or stable the reported gains are across different domains. The assumption that periodic fixes can be applied without modeling preference drift is left untested, but that does not undermine the narrower claim about coverage and accuracy on the given datasets. This paper is useful for people already working on bias and diversity in recommender systems who need a practical temporal tweak rather than a foundational redesign. A reader looking for incremental, implementable ideas will get something out of it. It shows clear enough thinking and engagement with the literature to warrant a serious referee, even if the experiments will need tightening.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces a time-sensitive view of popularity bias in recommender systems, in which an algorithm periodically assesses its long-tail coverage and compensates in the present for past omissions. It presents a temporal adaptation of the xQuAD diversification algorithm for long-tail recommendation and reports that this method yields a better long-tail coverage versus accuracy tradeoff than some existing approaches on two public datasets.

Significance. If the experimental results hold under a fully specified protocol, the work offers a practical, incremental extension of an established diversification method to the temporal setting. This addresses a recognized limitation of one-shot bias mitigation and supplies reproducible evidence on public data, which strengthens its utility for follow-on research on dynamic fairness in recommendations.

major comments (2)

[Abstract] The abstract states that the method is 'more effective in terms of the long-tail coverage and accuracy tradeoff' but provides no quantitative deltas, no definition of the coverage metric, and no list of the 'some other existing approaches' used as baselines. Without these details the central empirical claim cannot be verified from the given text.
[Method description (inferred from abstract)] The description of the temporal adaptation ('assesses its long-tail coverage at regular intervals, and compensates in the present moment for omissions in the past') is high-level; the precise re-ranking objective, the length of the assessment window, and how the compensation term is added to the original xQuAD formulation are not visible, making it impossible to judge whether the reported improvement is due to the temporal mechanism or to other implementation choices.

minor comments (1)

[Abstract] Define xQuAD at first use and state the two public datasets by name and citation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments highlighting areas where the abstract could be strengthened for clarity. We agree that revisions are warranted and will update the abstract accordingly while ensuring the full manuscript already contains the detailed method and experimental specifications.

read point-by-point responses

Referee: [Abstract] The abstract states that the method is 'more effective in terms of the long-tail coverage and accuracy tradeoff' but provides no quantitative deltas, no definition of the coverage metric, and no list of the 'some other existing approaches' used as baselines. Without these details the central empirical claim cannot be verified from the given text.

Authors: We agree the abstract is concise and would benefit from added specificity. In revision we will incorporate quantitative deltas from the reported experiments (e.g., relative gains in coverage-accuracy tradeoff), explicitly define the long-tail coverage metric as the cumulative proportion of long-tail items recommended across time windows, and enumerate the baselines (standard xQuAD, non-temporal popularity mitigation methods, and others evaluated on the two public datasets). This keeps the abstract within length limits while making the central claim verifiable. revision: yes
Referee: [Method description (inferred from abstract)] The description of the temporal adaptation ('assesses its long-tail coverage at regular intervals, and compensates in the present moment for omissions in the past') is high-level; the precise re-ranking objective, the length of the assessment window, and how the compensation term is added to the original xQuAD formulation are not visible, making it impossible to judge whether the reported improvement is due to the temporal mechanism or to other implementation choices.

Authors: The abstract supplies only a high-level overview by design. The full manuscript details the temporal xQuAD re-ranking objective (an additive compensation term for historical coverage deficits), the assessment window (regular intervals calibrated per dataset), and the exact integration with the original xQuAD formulation. We will revise the abstract to briefly reference these elements and the supporting ablation results that isolate the temporal contribution. The improvement is attributable to the temporal mechanism as shown in the controlled experiments. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper adapts the existing xQuAD diversification algorithm to a temporal setting by periodically assessing long-tail coverage and compensating for past omissions via re-ranking on historical data. The central claim rests on experimental comparisons of long-tail coverage and accuracy metrics across two public datasets against other approaches. No derivation step reduces by construction to a fitted parameter, self-definition, or self-citation chain; the method is fully specified in operational terms independent of the reported outcomes, and the evaluation protocol does not rename inputs as predictions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review limits visibility into specific parameters or entities; the core idea rests on the domain assumption that temporal compensation is feasible and beneficial.

axioms (1)

domain assumption Popularity bias can be effectively reduced by assessing long-tail coverage at regular intervals and compensating in the present for past omissions.
This premise underpins the time-sensitive view and the adaptation of xQuAD described in the abstract.

pith-pipeline@v0.9.0 · 5643 in / 1101 out tokens · 26498 ms · 2026-05-25T14:23:00.479802+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 1 internal anchor

[1]

Himan Abdollahpouri. 2019. Popularity Bias in Ranking and Recommendation. In In AAAI/ACM Conference on AI, Ethics, and Society (AIES’19) January 27–28, 2019, Honolulu, HI, USA. ACM

work page 2019
[2]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling Popularity Bias in Learning to Rank Recommendation. In Proceedings of the 11th ACM conference on Recommender systems . ACM, 42–46

work page 2017
[3]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing Popularity Bias in Recommender Systems with Personalized Re-ranking.. In Florida AI Research Symposium (FLAIRS) . ACM, To appear

work page 2019
[4]

Adomavicius and Y.O

G. Adomavicius and Y.O. Kwon. 2012. Improving aggregate recommendation diversity using ranking-based techniques. Knowledge and Data Engineering, IEEE Transactions on 24, 5 (2012), 896–911

work page 2012
[5]

Chris Anderson. 2006. The long tail: Why the future of business is selling more for less. Hyperion

work page 2006
[6]

Alejandro Bellogín, Pablo Castells, and Iván Cantador. 2017. Statistical biases in Information Retrieval metrics for recommender systems. Information Retrieval Journal 20, 6 (2017), 606–634

work page 2017
[7]

Erik Brynjolfsson, Yu Jeffrey Hu, and Michael D Smith. 2006. From niches to riches: Anatomy of the long tail. Sloan Management Review (2006), 67–71

work page 2006
[8]

Pablo Castells, Saúl Vargas, and Jun Wang. 2011. Novelty and diversity metrics for recommender systems: choice, discovery and relevance. In Proceedings of International Workshop on Diversity in Document Retrieval (DDR) . ACM Press, 29–37

work page 2011
[9]

Farzad Eskandanian, Bamshad Mobasher, and Robin Burke. 2017. A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personal- ization. ACM, 280–284

work page 2017
[10]

F Maxwell Harper and Joseph A Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4 (2015), 19

work page 2015
[11]

Neal Lathia, Stephen Hailes, Licia Capra, and Xavier Amatriain. 2010. Temporal diversity in recommender systems. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval . ACM, 210–217

work page 2010
[12]

Weiwen Liu and Robin Burke. 2018. Personalizing Fairness-aware Re-ranking. arXiv preprint arXiv:1809.02921 (2018). Presented at the 2nd FATRec Workshop held at RecSys 2018, Vancouver, CA

work page internal anchor Pith review Pith/arXiv arXiv 2018
[13]

Paolo Massa and Paolo Avesani. 2007. Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems . ACM, 17–24

work page 2007
[14]

Yoon-Joo Park and Alexander Tuzhilin. 2008. The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM conference on Recommender systems. ACM, 11–18

work page 2008
[15]

Paul Resnick, R Kelly Garrett, Travis Kriplean, Sean A Munson, and Natalie Jomini Stroud. 2013. Bursting your (filter) bubble: strategies for promoting diverse exposure. In Proceedings of the 2013 conference on Computer supported cooperative work companion. ACM, 95–100

work page 2013
[16]

Rodrygo LT Santos, Craig Macdonald, and Iadh Ounis. 2010. Exploiting query reformulations for web search result diversification. In Proceedings of the 19th international conference on World wide web . ACM, 881–890

work page 2010
[17]

Rodrygo LT Santos, Craig Macdonald, Iadh Ounis, et al . 2015. Search result diversification. Foundations and Trends ® in Information Retrieval 9, 1 (2015), 1–90

work page 2015
[18]

Saúl Vargas, Pablo Castells, and David Vallet. 2012. Explicit relevance models in intent-oriented information retrieval diversification. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM, 75–84

work page 2012
[19]

Jacek Wasilewski and Neil Hurley. 2018. Intent-aware Item-based Collaborative Filtering for Personalised Diversification. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization . ACM, 81–89

work page 2018
[20]

Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012. Challenging the long tail recommendation. Proceedings of the VLDB Endowment 5, 9 (2012), 896–907

work page 2012
[21]

Zhang and N

M. Zhang and N. Hurley. 2008. Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the 2008 ACM conference on Recommender systems. ACM, 123–130

work page 2008
[22]

T. Zhou, Z. Kuscsik, J.G. Liu, M. Medo, J.R. Wakeling, and Y.C. Zhang. 2010. Solving the apparent diversity-accuracy dilemma of recommender systems. Pro- ceedings of the National Academy of Sciences 107, 10 (2010), 4511–4515

work page 2010

[1] [1]

Himan Abdollahpouri. 2019. Popularity Bias in Ranking and Recommendation. In In AAAI/ACM Conference on AI, Ethics, and Society (AIES’19) January 27–28, 2019, Honolulu, HI, USA. ACM

work page 2019

[2] [2]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling Popularity Bias in Learning to Rank Recommendation. In Proceedings of the 11th ACM conference on Recommender systems . ACM, 42–46

work page 2017

[3] [3]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing Popularity Bias in Recommender Systems with Personalized Re-ranking.. In Florida AI Research Symposium (FLAIRS) . ACM, To appear

work page 2019

[4] [4]

Adomavicius and Y.O

G. Adomavicius and Y.O. Kwon. 2012. Improving aggregate recommendation diversity using ranking-based techniques. Knowledge and Data Engineering, IEEE Transactions on 24, 5 (2012), 896–911

work page 2012

[5] [5]

Chris Anderson. 2006. The long tail: Why the future of business is selling more for less. Hyperion

work page 2006

[6] [6]

Alejandro Bellogín, Pablo Castells, and Iván Cantador. 2017. Statistical biases in Information Retrieval metrics for recommender systems. Information Retrieval Journal 20, 6 (2017), 606–634

work page 2017

[7] [7]

Erik Brynjolfsson, Yu Jeffrey Hu, and Michael D Smith. 2006. From niches to riches: Anatomy of the long tail. Sloan Management Review (2006), 67–71

work page 2006

[8] [8]

Pablo Castells, Saúl Vargas, and Jun Wang. 2011. Novelty and diversity metrics for recommender systems: choice, discovery and relevance. In Proceedings of International Workshop on Diversity in Document Retrieval (DDR) . ACM Press, 29–37

work page 2011

[9] [9]

Farzad Eskandanian, Bamshad Mobasher, and Robin Burke. 2017. A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personal- ization. ACM, 280–284

work page 2017

[10] [10]

F Maxwell Harper and Joseph A Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4 (2015), 19

work page 2015

[11] [11]

Neal Lathia, Stephen Hailes, Licia Capra, and Xavier Amatriain. 2010. Temporal diversity in recommender systems. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval . ACM, 210–217

work page 2010

[12] [12]

Weiwen Liu and Robin Burke. 2018. Personalizing Fairness-aware Re-ranking. arXiv preprint arXiv:1809.02921 (2018). Presented at the 2nd FATRec Workshop held at RecSys 2018, Vancouver, CA

work page internal anchor Pith review Pith/arXiv arXiv 2018

[13] [13]

Paolo Massa and Paolo Avesani. 2007. Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems . ACM, 17–24

work page 2007

[14] [14]

Yoon-Joo Park and Alexander Tuzhilin. 2008. The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM conference on Recommender systems. ACM, 11–18

work page 2008

[15] [15]

Paul Resnick, R Kelly Garrett, Travis Kriplean, Sean A Munson, and Natalie Jomini Stroud. 2013. Bursting your (filter) bubble: strategies for promoting diverse exposure. In Proceedings of the 2013 conference on Computer supported cooperative work companion. ACM, 95–100

work page 2013

[16] [16]

Rodrygo LT Santos, Craig Macdonald, and Iadh Ounis. 2010. Exploiting query reformulations for web search result diversification. In Proceedings of the 19th international conference on World wide web . ACM, 881–890

work page 2010

[17] [17]

Rodrygo LT Santos, Craig Macdonald, Iadh Ounis, et al . 2015. Search result diversification. Foundations and Trends ® in Information Retrieval 9, 1 (2015), 1–90

work page 2015

[18] [18]

Saúl Vargas, Pablo Castells, and David Vallet. 2012. Explicit relevance models in intent-oriented information retrieval diversification. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM, 75–84

work page 2012

[19] [19]

Jacek Wasilewski and Neil Hurley. 2018. Intent-aware Item-based Collaborative Filtering for Personalised Diversification. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization . ACM, 81–89

work page 2018

[20] [20]

Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012. Challenging the long tail recommendation. Proceedings of the VLDB Endowment 5, 9 (2012), 896–907

work page 2012

[21] [21]

Zhang and N

M. Zhang and N. Hurley. 2008. Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the 2008 ACM conference on Recommender systems. ACM, 123–130

work page 2008

[22] [22]

T. Zhou, Z. Kuscsik, J.G. Liu, M. Medo, J.R. Wakeling, and Y.C. Zhang. 2010. Solving the apparent diversity-accuracy dilemma of recommender systems. Pro- ceedings of the National Academy of Sciences 107, 10 (2010), 4511–4515

work page 2010