# Exact-K Recommendation via Maximal Clique Optimization

arXiv: https://arxiv.org/abs/1905.07089 · HTML: https://mlsanity.com/paper/1905.07089/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 14 of 14)

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- [Value Penalized Q-Learning for Recommender Systems](https://mlsanity.com/paper/2110.07923/) (2021)
- [Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation](https://mlsanity.com/paper/2409.07416/) (2024)
- [Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints](https://mlsanity.com/paper/2308.12680/) (2023)
- [Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation](https://mlsanity.com/paper/2310.19536/) (2023)
- [Non-autoregressive Personalized Bundle Generation](https://mlsanity.com/paper/2406.06925/) (2024)
- [Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce](https://mlsanity.com/paper/2406.14004/) (2024)
- [Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce](https://mlsanity.com/paper/2408.00346/) (2024)

## Abstract (arXiv)

This paper targets to a novel but practical recommendation problem named exact-K recommendation. It is different from traditional top-K recommendation, as it focuses more on (constrained) combinatorial optimization which will optimize to recommend a whole set of K items called card, rather than ranking optimization which assumes that "better" items should be put into top positions. Thus we take the first step to give a formal problem definition, and innovatively reduce it to Maximum Clique Optimization based on graph. To tackle this specific combinatorial optimization problem which is NP-hard, we propose Graph Attention Networks (GAttN) with a Multi-head Self-attention encoder and a decoder with attention mechanism. It can end-to-end learn the joint distribution of the K items and generate an optimal card rather than rank individual items by prediction scores. Then we propose Reinforcement Learning from Demonstrations (RLfD) which combines the advantages in behavior cloning and reinforcement learning, making it sufficient- and-efficient to train the model. Extensive experiments on three datasets demonstrate the effectiveness of our proposed GAttN with RLfD method, it outperforms several strong baselines with a relative improvement of 7.7% and 4.7% on average in Precision and Hit Ratio respectively, and achieves state-of-the-art (SOTA) performance for the exact-K recommendation problem.