# Deep Adaptive Interest Network: Personalized Recommendation with Context-Aware Learning

arXiv: https://arxiv.org/abs/2409.02425 · HTML: https://mlsanity.com/paper/2409.02425/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Constrained Attractor Selection Using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.10500/) (2019)
- [Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.01255/) (2020)
- [A Model-Free Sampling Method for Estimating Basins of Attraction Using Hybrid Active Learning (HAL)](https://mlsanity.com/paper/2003.10976/) (2020)

## Built on by (most cited first, 3 of 3)

- [Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy](https://mlsanity.com/paper/2504.08751/) (2025)
- [Detecting and Classifying Defective Products in Images Using YOLO](https://mlsanity.com/paper/2412.16935/) (2024)
- [Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models](https://mlsanity.com/paper/2412.18715/) (2024)

## Abstract (arXiv)

In personalized recommendation systems, accurately capturing users' evolving interests and combining them with contextual information is a critical research area. This paper proposes a novel model called the Deep Adaptive Interest Network (DAIN), which dynamically models users' interests while incorporating context-aware learning mechanisms to achieve precise and adaptive personalized recommendations. DAIN leverages deep learning techniques to build an adaptive interest network structure that can capture users' interest changes in real-time while further optimizing recommendation results by integrating contextual information. Experiments conducted on several public datasets demonstrate that DAIN excels in both recommendation performance and computational efficiency. This research not only provides a new solution for personalized recommendation systems but also offers fresh insights into the application of context-aware learning in recommendation systems.