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Found 36 papers, 18 papers with code
Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation
We validate the effectiveness of PREC through both offline evaluation on public datasets and online A/B testing in an industrial application.
Improving Retrospective Language Agents via Joint Policy Gradient Optimization
In recent research advancements within the community, large language models (LLMs) have sparked great interest in creating autonomous agents.
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
Large language models (LLMs) are increasingly leveraged as foundational backbones in the development of advanced recommender systems, offering enhanced capabilities through their extensive knowledge and reasoning.
A Survey of Personalized Large Language Models: Progress and Future Directions
Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing styles, and preferences.
Evaluating Conversational Recommender Systems with Large Language Models: A User-Centric Evaluation Framework
Conversational recommender systems (CRS) involve both recommendation and dialogue tasks, which makes their evaluation a unique challenge.
MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation
To address these challenges, we propose a Model-Agnostic Joint Learning (MAJL) framework for both tasks.
MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants
LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries.
A Survey on the Memory Mechanism of Large Language Model based Agents
Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions.
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests.
Benchmarking News Recommendation in the Era of Green AI
Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems.
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy
To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels.
Only Encode Once: Making Content-based News Recommender Greener
Large pretrained language models (PLM) have become de facto news encoders in modern news recommender systems, due to their strong ability in comprehending textual content.
Out-of-distribution Detection with Implicit Outlier Transformation
It leads to a min-max learning scheme -- searching to synthesize OOD data that leads to worst judgments and learning from such OOD data for uniform performance in OOD detection.
REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation
To alleviate the above problems, we propose to build an explainable recommendation dataset with multi-aspect real user labeled ground truths.
Domain Adaptive Graph Infomax via Conditional Adversarial Networks
The emerging graph neural networks (GNNs) have demonstrated impressive performance on the node classification problem in complex networks.
A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction
However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.
Recommendation with User Active Disclosing Willingness
Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production. Traditional recommender models mostly collect as comprehensive as possible user behaviors for accurate preference estimation.
Debiased Recommendation with Neural Stratification
Debiased recommender models have recently attracted increasing attention from the academic and industry communities.
Multiple Robust Learning for Recommendation
Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate.
BARS: Towards Open Benchmarking for Recommender Systems
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Deep learning-based recommendation has become a widely adopted technique in various online applications.
Sequential Recommendation with Causal Behavior Discovery
The key of sequential recommendation lies in the accurate item correlation modeling.
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.
An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration
Conversion rate (CVR) prediction is becoming in- creasingly important in the multi-billion dollar on- line display advertising industry.
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored.
Ranked #4 on
Collaborative Filtering
on Yelp2018
Top-N Recommendation with Counterfactual User Preference Simulation
To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
The high cost of data labeling often results in node label shortage in real applications.
Network Together: Node Classification via Cross-Network Deep Network Embedding
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Adversarial Deep Network Embedding for Cross-network Node Classification
This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information.
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on Graphs
Graph convolutional neural networks (GCN) have been the model of choice for graph representation learning, which is mainly due to the effective design of graph convolution that computes the representation of a node by aggregating those of its neighbors.
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Existing methods for single network learning cannot solve this problem due to the domain shift across networks.
Adversarial Training Methods for Network Embedding
To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain.
Network Together: Node Classification via Cross network Deep Network Embedding
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Social and Information Networks
Adversarial Network Embedding
Learning low-dimensional representations of networks has proved effective in a variety of tasks such as node classification, link prediction and network visualization.
