# A Re-visit of the Popularity Baseline in Recommender Systems

arXiv: https://arxiv.org/abs/2005.13829 · HTML: https://mlsanity.com/paper/2005.13829/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 18 of 18)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling](https://mlsanity.com/paper/2308.07192/) (2023)
- [Causal Collaborative Filtering](https://mlsanity.com/paper/2102.01868/) (2021)
- [Robust Collaborative Filtering to Popularity Distribution Shift](https://mlsanity.com/paper/2310.10696/) (2023)
- [Knowledge Graph Enhanced Language Agents for Recommendation](https://mlsanity.com/paper/2410.19627/) (2024)
- [Yambda-5B -- A Large-Scale Multi-modal Dataset for Ranking And Retrieval](https://mlsanity.com/paper/2505.22238/) (2025)
- [When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?](https://mlsanity.com/paper/2305.01801/) (2023)
- [Selection, Not Salience: The Shape and Limits of Personalization in Social Highlighting](https://mlsanity.com/paper/2606.10398/) (2026)
- [The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience](https://mlsanity.com/paper/2606.11654/) (2026)
- [Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation](https://mlsanity.com/paper/2308.08799/) (2023)
- [LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce](https://mlsanity.com/paper/2603.11025/) (2026)
- [Next Period Recommendation Reality Check](https://mlsanity.com/paper/2110.05589/) (2021)
- [CVTT: Cross-Validation Through Time](https://mlsanity.com/paper/2205.05393/) (2022)
- [Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents](https://mlsanity.com/paper/2301.00152/) (2022)
- [Rolling Forward: Enhancing LightGCN with Causal Graph Convolution for Credit Bond Recommendation](https://mlsanity.com/paper/2503.14213/) (2025)
- [Cyberswarm: a novel swarm intelligence algorithm inspired by cyber community dynamics](https://mlsanity.com/paper/2512.14752/) (2025)
- [Trait, Not State: The Durability of Reading Identity in Social Highlighting](https://mlsanity.com/paper/2606.12904/) (2026)
- [Continual Graph Memory for Adaptive Recommendation under Intent Drift](https://mlsanity.com/paper/2609.04651/) (2026)

## Abstract (arXiv)

Popularity is often included in experimental evaluation to provide a reference performance for a recommendation task. To understand how popularity baseline is defined and evaluated, we sample 12 papers from top-tier conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits. We note that the widely adopted MostPop baseline simply ranks items based on the number of interactions in the training data. We argue that the current evaluation of popularity (i) does not reflect the popular items at the time when a user interacts with the system, and (ii) may recommend items released after a user's last interaction with the system. On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system. We further show that, on MovieLens dataset, the users having lower tendencies on movies tend to follow the crowd and rate more popular movies. Movie lovers who rate a large number of movies, rate movies based on their own preferences and interests. Through this study, we call for a re-visit of the popularity baseline in recommender system to better reflect its effectiveness.