mlsanityData to 7 Oct 2026

Recent Advances in Heterogeneous Relation Learning for Recommendation

arXiv 2110.03455 ↗First posted 7 Oct 2021cs.IR cs.LGcited by 13

Not on the map. Why?

Abstract (arXiv)

Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload. In this survey, we review the development of recommendation frameworks with the focus on heterogeneous relational learning, which consists of different types of dependencies among users and items. The objective of this task is to map heterogeneous relational data into latent representation space, such that the structural and relational properties from both user and item domain can be well preserved. To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation. We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information. Finally, we present an exploratory outlook to highlight several promising directions and opportunities in heterogeneous relational learning frameworks for recommendation.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 20 of its 71 references that are in the map. First posted on arXiv 7 Oct 2021.

Builds on

20 of 71 references.

References · 20

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  4. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  5. 2019Graph Neural Networks for Social Recommendationcited by 711
  6. 2019Neural Graph Collaborative Filteringcited by 660
  7. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
  8. 2018Variational Autoencoders for Collaborative Filteringcited by 308
  9. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  10. 2020Disentangled Graph Collaborative Filteringcited by 118
  11. 2019Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systemscited by 107
  12. 2019Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferencescited by 87
8 more
  1. 2019Session-based Social Recommendation via Dynamic Graph Attention Networkscited by 76
  2. 2019A Neural Influence Diffusion Model for Social Recommendationcited by 69
  3. 2019Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systemscited by 41
  4. 2019Jointly Learning Explainable Rules for Recommendation with Knowledge Graphcited by 40
  5. 2016A Neural Autoregressive Approach to Collaborative Filteringcited by 23
  6. 2019Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendationcited by 23
  7. 2018Learning to Recommend with Multiple Cascading Behaviorscited by 16
  8. 2016Collaborative Filtering with User-Item Co-Autoregressive Modelscited by 8

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 8; 2023: 2; 2024: 1.

13 papers.

Sort:

  1. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 69
  2. 2022Hypergraph Contrastive Collaborative Filteringcited by 63
  3. 2022Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Predictioncited by 39
  4. 2021Graph Meta Network for Multi-Behavior Recommendationcited by 38
  5. 2022Contrastive Meta Learning with Behavior Multiplicity for Recommendationcited by 37
  6. 2022Self-Supervised Hypergraph Transformer for Recommender Systemscited by 32
  7. 2022Multiplex Heterogeneous Graph Convolutional Networkcited by 27
  8. 2022Multi-Behavior Sequential Recommendation with Temporal Graph Transformercited by 20
  9. 2021Social Recommendation with Self-Supervised Metagraph Informax Networkcited by 18
  10. 2022Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendationcited by 6
  11. 2023Instant Representation Learning for Recommendation over Large Dynamic Graphscited by 6
  12. 2023Compressed Interaction Graph based Framework for Multi-behavior Recommendationcited by 4
1 more
  1. 2024Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learningcited by 1