# Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations

arXiv: https://arxiv.org/abs/2408.03772 · HTML: https://mlsanity.com/paper/2408.03772/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 67 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017)
- [KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems](https://mlsanity.com/paper/2202.10842/) (2022)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://mlsanity.com/paper/2211.10486/) (2022)
- [Personalized News Recommendation: Methods and Challenges](https://mlsanity.com/paper/2106.08934/) (2021)
- [DGCN: Diversified Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2108.06952/) (2021)
- [Measuring Recommender System Effects with Simulated Users](https://mlsanity.com/paper/2101.04526/) (2021)
- [Recommending Accurate and Diverse Items Using Bilateral Branch Network](https://mlsanity.com/paper/2101.00781/) (2021)
- [YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendations](https://mlsanity.com/paper/2203.10666/) (2022)
- [Neural Gaussian Copula for Variational Autoencoder](https://mlsanity.com/paper/1909.03569/) (2019)
- [Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions](https://mlsanity.com/paper/2204.10729/) (2022)
- [Neural Copula: A unified framework for estimating generic high-dimensional Copula functions](https://mlsanity.com/paper/2205.15031/) (2022)
- [Copula for Instance-wise Feature Selection and Ranking](https://mlsanity.com/paper/2308.00549/) (2023)

## Built on by (most cited first, 11 of 11)

- [Prompt-to-Slate: Diffusion Models for Prompt-Conditioned Slate Generation](https://mlsanity.com/paper/2408.06883/) (2024)
- [Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences](https://mlsanity.com/paper/2409.16478/) (2024)
- [CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations](https://mlsanity.com/paper/2604.14586/) (2026)
- [MUSS: Multilevel Subset Selection for Relevance and Diversity](https://mlsanity.com/paper/2503.11126/) (2025)
- [Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation](https://mlsanity.com/paper/2512.03067/) (2025)
- [Unexpected Knowledge: Auditing Wikipedia and Grokipedia Search Recommendations](https://mlsanity.com/paper/2512.17027/) (2025)
- [Tree of Preferences for Diversified Recommendation](https://mlsanity.com/paper/2601.02386/) (2025)
- [Diversified recommendations of cultural activities with personalized determinantal point processes](https://mlsanity.com/paper/2509.10392/) (2025)
- [When Relevance Meets Novelty: Dual-Stable Periodic Optimization for Serendipitous Recommendation](https://mlsanity.com/paper/2508.00450/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)
- [Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions](https://mlsanity.com/paper/2607.11555/) (2026)

## Abstract (arXiv)

Providing recommendations that are both relevant and diverse is a key consideration of modern recommender systems. Optimizing both of these measures presents a fundamental trade-off, as higher diversity typically comes at the cost of relevance, resulting in lower user engagement. Existing recommendation algorithms try to resolve this trade-off by combining the two measures, relevance and diversity, into one aim and then seeking recommendations that optimize the combined objective, for a given number of items to recommend. Traditional approaches, however, do not consider the user interaction with the recommended items. In this paper, we put the user at the central stage, and build on the interplay between relevance, diversity, and user behavior. In contrast to applications where the goal is solely to maximize engagement, we focus on scenarios aiming at maximizing the total amount of knowledge encountered by the user. We use diversity as a surrogate of the amount of knowledge obtained by the user while interacting with the system, and we seek to maximize diversity. We propose a probabilistic user-behavior model in which users keep interacting with the recommender system as long as they receive relevant recommendations, but they may stop if the relevance of the recommended items drops. Thus, for a recommender system to achieve a high-diversity measure, it will need to produce recommendations that are both relevant and diverse. Finally, we propose a novel recommendation strategy that combines relevance and diversity by a copula function. We conduct an extensive evaluation of the proposed methodology over multiple datasets, and we show that our strategy outperforms several state-of-the-art competitors. Our implementation is publicly available at https://github.com/EricaCoppolillo/EXPLORE.