# Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating

arXiv: https://arxiv.org/abs/2310.03813 · HTML: https://mlsanity.com/paper/2310.03813/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 77 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 30 of 38)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling](https://mlsanity.com/paper/2110.08263/) (2021)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer](https://mlsanity.com/paper/2105.11741/) (2021)
- [Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2112.08679/) (2021)
- [SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation](https://mlsanity.com/paper/2202.03104/) (2022)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [Contrastive Learning for Cold-Start Recommendation](https://mlsanity.com/paper/2107.05315/) (2021)
- [LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2302.08191/) (2023)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion](https://mlsanity.com/paper/1905.01866/) (2019)
- [Towards Representation Alignment and Uniformity in Collaborative Filtering](https://mlsanity.com/paper/2206.12811/) (2022)
- [Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems](https://mlsanity.com/paper/2005.12964/) (2020)
- [Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translation](https://mlsanity.com/paper/2103.02262/) (2021)
- [Heterogeneous Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2303.00995/) (2023)
- [CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation](https://mlsanity.com/paper/2206.00242/) (2022)
- [Bundle Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2005.03475/) (2020)
- [Multi-view Intent Disentangle Graph Networks for Bundle Recommendation](https://mlsanity.com/paper/2202.11425/) (2022)
- [Personalized Bundle List Recommendation](https://mlsanity.com/paper/1904.01933/) (2019)
- [Personalized Bundle Recommendation in Online Games](https://mlsanity.com/paper/2104.05307/) (2021)
- [Contrastive Collaborative Filtering for Cold-Start Item Recommendation](https://mlsanity.com/paper/2302.02151/) (2023)
- [Curriculum Learning with Hindsight Experience Replay for Sequential Object Manipulation Tasks](https://mlsanity.com/paper/2008.09377/) (2020)
- [Bundle MCR: Towards Conversational Bundle Recommendation](https://mlsanity.com/paper/2207.12628/) (2022)
- [A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists](https://mlsanity.com/paper/1912.13023/) (2019)
- [Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding](https://mlsanity.com/paper/2205.14970/) (2022)

## Built on by (most cited first, 4 of 4)

- [Personalized Diffusion Model Reshapes Cold-Start Bundle Recommendation](https://mlsanity.com/paper/2505.14901/) (2025)
- [Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?](https://mlsanity.com/paper/2504.17220/) (2025)
- [Dual-Enhancement Product Bundling: Bridging Interactive Graph and Large Language Model](https://mlsanity.com/paper/2604.14030/) (2026)
- [Denoising Implicit Feedback for Cold-start Recommendation](https://mlsanity.com/paper/2606.19658/) (2026)

## Abstract (arXiv)

How can we recommend cold-start bundles to users? The cold-start problem in bundle recommendation is crucial because new bundles are continuously created on the Web for various marketing purposes. Despite its importance, existing methods for cold-start item recommendation are not readily applicable to bundles. They depend overly on historical information, even for less popular bundles, failing to address the primary challenge of the highly skewed distribution of bundle interactions. In this work, we propose CoHeat (Popularity-based Coalescence and Curriculum Heating), an accurate approach for cold-start bundle recommendation. CoHeat first represents users and bundles through graph-based views, capturing collaborative information effectively. To estimate the user-bundle relationship more accurately, CoHeat addresses the highly skewed distribution of bundle interactions through a popularity-based coalescence approach, which incorporates historical and affiliation information based on the bundle's popularity. Furthermore, it effectively learns latent representations by exploiting curriculum learning and contrastive learning. CoHeat demonstrates superior performance in cold-start bundle recommendation, achieving up to 193% higher nDCG@20 compared to the best competitor.