# Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach

arXiv: https://arxiv.org/abs/2005.12021 · HTML: https://mlsanity.com/paper/2005.12021/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 30 of 30)

- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Defending against Model Stealing via Verifying Embedded External Features](https://mlsanity.com/paper/2112.03476/) (2021)
- [Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models](https://mlsanity.com/paper/2201.09370/) (2022)
- [GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations](https://mlsanity.com/paper/2208.04222/) (2022)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering](https://mlsanity.com/paper/2402.11523/) (2024)
- [SiReN: Sign-Aware Recommendation Using Graph Neural Networks](https://mlsanity.com/paper/2108.08735/) (2021)
- [Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents](https://mlsanity.com/paper/2203.02898/) (2022)
- [Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining](https://mlsanity.com/paper/2303.15311/) (2023)
- [Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation](https://mlsanity.com/paper/2105.07377/) (2021)
- [Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios](https://mlsanity.com/paper/2504.16352/) (2025)
- [Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data](https://mlsanity.com/paper/2407.18564/) (2024)
- [LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching](https://mlsanity.com/paper/2108.02915/) (2021)
- [Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education](https://mlsanity.com/paper/2403.05559/) (2024)
- [Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction](https://mlsanity.com/paper/2106.00263/) (2021)
- [Heterogeneous Hypergraph Embedding for Recommendation Systems](https://mlsanity.com/paper/2407.03665/) (2024)
- [RAID: An In-Training Defense against Attribute Inference Attacks in Recommender Systems](https://mlsanity.com/paper/2504.11510/) (2025)
- [DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching](https://mlsanity.com/paper/2106.04905/) (2021)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations](https://mlsanity.com/paper/2501.06686/) (2025)
- [A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings](https://mlsanity.com/paper/2502.10875/) (2025)
- [LEGO: A Lightweight and Efficient Multiple-Attribute Unlearning Framework for Recommender Systems](https://mlsanity.com/paper/2510.20327/) (2025)

## Abstract (arXiv)

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item annotation, and feature-enhanced recommendation. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. Therefore, item recommendation and attribute inference have become two main tasks in these platforms. Researchers have long converged that user (item) attributes and the preference behavior are highly correlated. Some researchers proposed to leverage one kind of data for the remaining task, and showed to improve performance. Nevertheless, these models either neglected the incompleteness of user (item) attributes or regarded the correlation of the two tasks with simple models, leading to suboptimal performance of these two tasks. To this end, in this paper, we define these two tasks in an attributed user-item bipartite graph, and propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference. The key idea of AGCN is to iteratively perform two parts: 1) Learning graph embedding parameters with previously learned approximated attribute values to facilitate two tasks; 2) Sending the approximated updated attribute values back to the attributed graph for better graph embedding learning. Therefore, AGCN could adaptively adjust the graph embedding learning parameters by incorporating both the given attributes and the estimated attribute values, in order to provide weakly supervised information to refine the two tasks. Extensive experimental results on three real-world datasets clearly show the effectiveness of the proposed model.