# Personalized Bundle List Recommendation

arXiv: https://arxiv.org/abs/1904.01933 · HTML: https://mlsanity.com/paper/1904.01933/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 21 of 21)

- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [Personalized Showcases: Generating Multi-Modal Explanations for Recommendations](https://mlsanity.com/paper/2207.00422/) (2022)
- [Diffusion Models for Generative Outfit Recommendation](https://mlsanity.com/paper/2402.17279/) (2024)
- [Multi-view Intent Disentangle Graph Networks for Bundle Recommendation](https://mlsanity.com/paper/2202.11425/) (2022)
- [Drug Package Recommendation via Interaction-aware Graph Induction](https://mlsanity.com/paper/2102.03577/) (2021)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Bundle MCR: Towards Conversational Bundle Recommendation](https://mlsanity.com/paper/2207.12628/) (2022)
- [Fine-tuning Multimodal Large Language Models for Product Bundling](https://mlsanity.com/paper/2407.11712/) (2024)
- [Adaptive In-Context Learning with Large Language Models for Bundle Generation](https://mlsanity.com/paper/2312.16262/) (2023)
- [Autoregressive Generation Strategies for Top-K Sequential Recommendations](https://mlsanity.com/paper/2409.17730/) (2024)
- [Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating](https://mlsanity.com/paper/2310.03813/) (2023)
- [Two Is Better Than One: Dual Embeddings for Complementary Product Recommendations](https://mlsanity.com/paper/2211.14982/) (2022)
- [Bundle Recommendation with Item-level Causation-enhanced Multi-view Learning](https://mlsanity.com/paper/2408.08906/) (2024)
- [Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?](https://mlsanity.com/paper/2504.17220/) (2025)
- [Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generation](https://mlsanity.com/paper/2508.17250/) (2025)
- [BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations](https://mlsanity.com/paper/2307.02797/) (2023)
- [Non-autoregressive Personalized Bundle Generation](https://mlsanity.com/paper/2406.06925/) (2024)
- [On a Scale-Invariant Approach to Bundle Recommendations in Candy Crush Saga](https://mlsanity.com/paper/2408.06799/) (2024)
- [Popularity Estimation and New Bundle Generation using Content and Context based Embeddings](https://mlsanity.com/paper/2412.17310/) (2024)
- [Producer-Fairness in Sequential Bundle Recommendation](https://mlsanity.com/paper/2506.20329/) (2025)
- [Recommending Composite Items Using Multi-Level Preference Information: A Joint Interaction Modeling Approach](https://mlsanity.com/paper/2601.19005/) (2026)

## Abstract (arXiv)

Product bundling, offering a combination of items to customers, is one of the marketing strategies commonly used in online e-commerce and offline retailers. A high-quality bundle generalizes frequent items of interest, and diversity across bundles boosts the user-experience and eventually increases transaction volume. In this paper, we formalize the personalized bundle list recommendation as a structured prediction problem and propose a bundle generation network (BGN), which decomposes the problem into quality/diversity parts by the determinantal point processes (DPPs). BGN uses a typical encoder-decoder framework with a proposed feature-aware softmax to alleviate the inadequate representation of traditional softmax, and integrates the masked beam search and DPP selection to produce high-quality and diversified bundle list with an appropriate bundle size. We conduct extensive experiments on three public datasets and one industrial dataset, including two generated from co-purchase records and the other two extracted from real-world online bundle services. BGN significantly outperforms the state-of-the-art methods in terms of quality, diversity and response time over all datasets. In particular, BGN improves the precision of the best competitors by 16% on average while maintaining the highest diversity on four datasets, and yields a 3.85x improvement of response time over the best competitors in the bundle list recommendation problem.