# Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation

arXiv: https://arxiv.org/abs/2409.17711 · HTML: https://mlsanity.com/paper/2409.17711/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Is ChatGPT a Good Recommender? A Preliminary Study](https://mlsanity.com/paper/2304.10149/) (2023)
- [Uncovering ChatGPT's Capabilities in Recommender Systems](https://mlsanity.com/paper/2305.02182/) (2023)
- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://mlsanity.com/paper/2101.05667/) (2021)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [Empowering News Recommendation with Pre-trained Language Models](https://mlsanity.com/paper/2104.07413/) (2021)
- [HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation](https://mlsanity.com/paper/2106.04408/) (2021)
- [PiRank: Scalable Learning To Rank via Differentiable Sorting](https://mlsanity.com/paper/2012.06731/) (2020)
- [Approximate Ranking from Pairwise Comparisons](https://mlsanity.com/paper/1801.01253/) (2018)
- [Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation](https://mlsanity.com/paper/2109.05446/) (2021)
- [UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation](https://mlsanity.com/paper/2305.15756/) (2023)
- [Generative Flow Network for Listwise Recommendation](https://mlsanity.com/paper/2306.02239/) (2023)
- [Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation](https://mlsanity.com/paper/2112.00944/) (2021)
- [QuoteR: A Benchmark of Quote Recommendation for Writing](https://mlsanity.com/paper/2202.13145/) (2022)
- [Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation](https://mlsanity.com/paper/2205.06058/) (2022)
- [Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations](https://mlsanity.com/paper/2106.09896/) (2021)
- [Sparse Pairwise Re-ranking with Pre-trained Transformers](https://mlsanity.com/paper/2207.04470/) (2022)
- [DIGAT: Modeling News Recommendation with Dual-Graph Interaction](https://mlsanity.com/paper/2210.05196/) (2022)
- [Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation](https://mlsanity.com/paper/2104.07404/) (2021)
- [Denoising Neural Network for News Recommendation with Positive and Negative Implicit Feedback](https://mlsanity.com/paper/2204.04397/) (2022)
- [DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning](https://mlsanity.com/paper/2106.06258/) (2021)

## Built on by (most cited first, 1 of 1)

- [SRLF: An Agent-Driven Set-Wise Reflective Learning Framework for Sequential Recommendation](https://mlsanity.com/paper/2511.11370/) (2025)

## Abstract (arXiv)

News recommendation is a challenging task that involves personalization based on the interaction history and preferences of each user. Recent works have leveraged the power of pretrained language models (PLMs) to directly rank news items by using inference approaches that predominately fall into three categories: pointwise, pairwise, and listwise learning-to-rank. While pointwise methods offer linear inference complexity, they fail to capture crucial comparative information between items that is more effective for ranking tasks. Conversely, pairwise and listwise approaches excel at incorporating these comparisons but suffer from practical limitations: pairwise approaches are either computationally expensive or lack theoretical guarantees, and listwise methods often perform poorly in practice. In this paper, we propose a novel framework for PLM-based news recommendation that integrates both pointwise relevance prediction and pairwise comparisons in a scalable manner. We present a rigorous theoretical analysis of our framework, establishing conditions under which our approach guarantees improved performance. Extensive experiments show that our approach outperforms the state-of-the-art methods on the MIND and Adressa news recommendation datasets.