# A Study of Position Bias in Digital Library Recommender Systems

arXiv: https://arxiv.org/abs/1802.06565 · HTML: https://mlsanity.com/paper/1802.06565/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 13 of 13)

- [Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness](https://mlsanity.com/paper/2110.08353/) (2021)
- [TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems](https://mlsanity.com/paper/2308.14355/) (2023)
- [Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce](https://mlsanity.com/paper/2307.16060/) (2023)
- [Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)](https://mlsanity.com/paper/2312.08520/) (2023)
- [What Factors Should Paper-Reviewer Assignments Rely On? Community Perspectives on Issues and Ideals in Conference Peer-Review](https://mlsanity.com/paper/2205.01005/) (2022)
- [Metrics for popularity bias in dynamic recommender systems](https://mlsanity.com/paper/2310.08455/) (2023)
- [Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation](https://mlsanity.com/paper/2301.07483/) (2023)
- [Exploring and Exploiting Data Heterogeneity in Recommendation](https://mlsanity.com/paper/2305.15431/) (2023)
- [The Importance of Causality in Decision Making: A Perspective on Recommender Systems](https://mlsanity.com/paper/2410.01822/) (2024)
- [(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)](https://mlsanity.com/paper/2312.08517/) (2023)
- [Adaptively Learning to Select-Rank in Online Platforms](https://mlsanity.com/paper/2406.05017/) (2024)
- [On the Necessity of World Knowledge for Mitigating Missing Labels in Extreme Classification](https://mlsanity.com/paper/2408.09585/) (2024)
- [OptAgent: Optimizing Query Rewriting for E-commerce via Multi-Agent Simulation](https://mlsanity.com/paper/2510.03771/) (2025)

## Abstract (arXiv)

"Position bias" describes the tendency of users to interact with items on top of a list with higher probability than with items at a lower position in the list, regardless of the items' actual relevance. In the domain of recommender systems, particularly recommender systems in digital libraries, position bias has received little attention. We conduct a study in a real-world recommender system that delivered ten million related-article recommendations to the users of the digital library Sowiport, and the reference manager JabRef. Recommendations were randomly chosen to be shuffled or non-shuffled, and we compared click-through rate (CTR) for each rank of the recommendations. According to our analysis, the CTR for the highest rank in the case of Sowiport is 53% higher than expected in a hypothetical non-biased situation (0.189% vs. 0.123%). Similarly, in the case of Jabref the highest rank received a CTR of 1.276%, which is 87% higher than expected (0.683%). A chi-squared test confirms the strong relationship between the rank of the recommendation shown to the user and whether the user decided to click it (p < 0.01 for both Jabref and Sowiport). Our study confirms the findings from other domains, that recommendations in the top positions are more often clicked, regardless of their actual relevance.