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Found 49 papers, 48 papers with code
Automated Self-Supervised Learning for Recommendation
This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation.
SSLRec: A Self-Supervised Learning Framework for Recommendation
Our SSLRec platform covers a comprehensive set of state-of-the-art SSL-enhanced recommendation models across different scenarios, enabling researchers to evaluate these cutting-edge models and drive further innovation in the field.
Representation Learning with Large Language Models for Recommendation
RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework.
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders.
Multi-Modal Self-Supervised Learning for Recommendation
The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and acoustic) into the latent user representations.
OpenGraph: Towards Open Graph Foundation Models
By effectively capturing the graph's underlying structure, these GNNs have shown great potential in enhancing performance in graph learning tasks, such as link prediction and node classification.
UrbanGPT: Spatio-Temporal Large Language Models
These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce.
Knowledge Graph Contrastive Learning for Recommendation
However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities.
GraphEdit: Large Language Models for Graph Structure Learning
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures.
Hypergraph Contrastive Collaborative Filtering
Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination.
Heterogeneous Graph Contrastive Learning for Recommendation
In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views.
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework.
A Comprehensive Survey on Self-Supervised Learning for Recommendation
Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences.
Graph Masked Autoencoder for Sequential Recommendation
While some powerful neural network architectures (e. g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios.
DiffKG: Knowledge Graph Diffusion Model for Recommendation
To bridge this research gap, we propose a novel knowledge graph diffusion model for recommendation, referred to as DiffKG.
Self-Supervised Hypergraph Transformer for Recommender Systems
With the distilled global context, a cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph, so as to enhance the robustness of recommender systems.
Disentangled Contrastive Collaborative Filtering
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF).
GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
This strategy guides the mask autoencoder in learning robust spatio-temporal representations and facilitates the modeling of different relationships, ranging from intra-cluster to inter-cluster, in an easy-to-hard training manner.
Graph Transformer for Recommendation
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture.
Knowledge Graph Self-Supervised Rationalization for Recommendation
By masking important knowledge with high rational scores, KGRec is trained to rebuild and highlight useful knowledge connections that serve as rationales.
Adaptive Graph Contrastive Learning for Recommendation
These approaches conduct self-supervised learning through creating contrastive views, but they depend on the tedious trial-and-error selection of augmentation methods.
Automated Spatio-Temporal Graph Contrastive Learning
In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources.
Graph Meta Network for Multi-Behavior Recommendation
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions.
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users.
Debiased Contrastive Learning for Sequential Recommendation
This solution is designed to tackle the popularity bias issue in recommendation systems.
GraphPro: Graph Pre-training and Prompt Learning for Recommendation
The temporal prompt mechanism encodes time information on user-item interaction, allowing the model to naturally capture temporal context, while the graph-structural prompt learning mechanism enables the transfer of pre-trained knowledge to adapt to behavior dynamics without the need for continuous incremental training.
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.
Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation
In addition, we introduce a cross-view contrastive learning paradigm to model the inter-dependencies across view-specific region representations and preserve underlying relation heterogeneity.
Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.
PromptMM: Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning
Additionally, to adjust the impact of inaccuracies in multimedia data, a disentangled multi-modal list-wise distillation is developed with modality-aware re-weighting mechanism.
Graph-less Collaborative Filtering
Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning.
Explainable Spatio-Temporal Graph Neural Networks
Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder.
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods.
Denoised Self-Augmented Learning for Social Recommendation
Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve user-item interaction modeling.
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.
How Expressive are Graph Neural Networks in Recommendation?
Most existing works adopt the graph isomorphism test as the metric of expressiveness, but this graph-level task may not effectively assess a model's ability in recommendation, where the objective is to distinguish nodes of different closeness.
Spatio-Temporal Meta Contrastive Learning
Although recent STGNN models with contrastive learning aim to address these challenges, most of them use pre-defined augmentation strategies that heavily depend on manual design and cannot be customized for different Spatio-Temporal Graph (STG) scenarios.
Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies.
Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks
Social recommendation which aims to leverage social connections among users to enhance the recommendation performance.
Social Recommendation with Self-Supervised Metagraph Informax Network
To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, em-powering SMIN to maintain dedicated representations for multi-faceted user- and item-wise dependencies.
Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
Due to the overlook of user's multi-behavioral patterns over different items, existing recommendation methods are insufficient to capture heterogeneous collaborative signals from user multi-behavior data.
Disentangled Graph Social Recommendation
In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections.
Graph Augmentation for Recommendation
To address these challenges, we propose a principled framework called GraphAug.
Multi-Relational Contrastive Learning for Recommendation
Personalized recommender systems play a crucial role in capturing users' evolving preferences over time to provide accurate and effective recommendations on various online platforms.
Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks.
Multi-Behavior Graph Neural Networks for Recommender System
Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder.
