Graph Learning based Recommender Systems: A Review
- URL: http://arxiv.org/abs/2105.06339v1
- Date: Thu, 13 May 2021 14:50:45 GMT
- Title: Graph Learning based Recommender Systems: A Review
- Authors: Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet
A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu
- Abstract summary: Graph Learning based Recommender Systems (GLRS) employ advanced graph learning approaches to model users' preferences and intentions as well as items' characteristics for recommendations.
We provide a systematic review of GLRS, by discussing how they extract important knowledge from graph-based representations to improve the accuracy, reliability and explainability of the recommendations.
- Score: 111.43249652335555
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent years have witnessed the fast development of the emerging topic of
Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph
learning approaches to model users' preferences and intentions as well as
items' characteristics for recommendations. Differently from other RS
approaches, including content-based filtering and collaborative filtering, GLRS
are built on graphs where the important objects, e.g., users, items, and
attributes, are either explicitly or implicitly connected. With the rapid
development of graph learning techniques, exploring and exploiting homogeneous
or heterogeneous relations in graphs are a promising direction for building
more effective RS. In this paper, we provide a systematic review of GLRS, by
discussing how they extract important knowledge from graph-based
representations to improve the accuracy, reliability and explainability of the
recommendations. First, we characterize and formalize GLRS, and then summarize
and categorize the key challenges and main progress in this novel research
area. Finally, we share some new research directions in this vibrant area.
Related papers
- Continual Learning on Graphs: Challenges, Solutions, and Opportunities [72.7886669278433]
We provide a comprehensive review of existing continual graph learning (CGL) algorithms.
We compare methods with traditional continual learning techniques and analyze the applicability of the traditional continual learning techniques to forgetting tasks.
We will maintain an up-to-date repository featuring a comprehensive list of accessible algorithms.
arXiv Detail & Related papers (2024-02-18T12:24:45Z) - A Survey of Data-Efficient Graph Learning [16.053913182723143]
We introduce a novel concept of Data-Efficient Graph Learning (DEGL) as a research frontier.
We systematically review recent advances on several key aspects, including self-supervised graph learning, semi-supervised graph learning, and few-shot graph learning.
arXiv Detail & Related papers (2024-02-01T09:28:48Z) - Graph Domain Adaptation: Challenges, Progress and Prospects [61.9048172631524]
We propose graph domain adaptation as an effective knowledge-transfer paradigm across graphs.
GDA introduces a bunch of task-related graphs as source graphs and adapts the knowledge learnt from source graphs to the target graphs.
We outline the research status and challenges, propose a taxonomy, introduce the details of representative works, and discuss the prospects.
arXiv Detail & Related papers (2024-02-01T02:44:32Z) - Counterfactual Learning on Graphs: A Survey [34.47646823407408]
Graph neural networks (GNNs) have achieved great success in representation learning on graphs.
Counterfactual learning on graphs has shown promising results in alleviating these drawbacks.
Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs.
arXiv Detail & Related papers (2023-04-03T21:42:42Z) - CogDL: A Comprehensive Library for Graph Deep Learning [55.694091294633054]
We present CogDL, a library for graph deep learning that allows researchers and practitioners to conduct experiments, compare methods, and build applications with ease and efficiency.
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
We develop efficient sparse operators for CogDL, enabling it to become the most competitive graph library for efficiency.
arXiv Detail & Related papers (2021-03-01T12:35:16Z) - Learning Intents behind Interactions with Knowledge Graph for
Recommendation [93.08709357435991]
Knowledge graph (KG) plays an increasingly important role in recommender systems.
Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents.
We propose a new model, Knowledge Graph-based Intent Network (KGIN)
arXiv Detail & Related papers (2021-02-14T03:21:36Z) - A Light Heterogeneous Graph Collaborative Filtering Model using Textual
Information [16.73333758538986]
We exploit the relevant and easily accessible textual information by advanced natural language processing (NLP) models.
We propose a light RGCN-based (RGCN, relational graph convolutional network) collaborative filtering method on heterogeneous graphs.
arXiv Detail & Related papers (2020-10-04T11:10:37Z) - Iterative Deep Graph Learning for Graph Neural Networks: Better and
Robust Node Embeddings [53.58077686470096]
We propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL) for jointly and iteratively learning graph structure and graph embedding.
Our experiments show that our proposed IDGL models can consistently outperform or match the state-of-the-art baselines.
arXiv Detail & Related papers (2020-06-21T19:49:15Z) - Deep Learning on Knowledge Graph for Recommender System: A Survey [36.41255991011155]
A knowledge graph is capable of encoding high-order relations that connect two objects with one or multiple related attributes.
With the help of the emerging Graph Neural Networks (GNN), it is possible to extract both object characteristics and relations from KG.
arXiv Detail & Related papers (2020-03-25T22:53:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.