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Causal inference in recommender systems: A survey and future directions
Recommender systems have become crucial in information filtering nowadays. Existing
recommender systems extract user preferences based on the correlation in data, such as …
recommender systems extract user preferences based on the correlation in data, such as …
A survey on popularity bias in recommender systems
Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …
promise of such systems is that they are able to increase the visibility of items in the long tail …
Tallrec: An effective and efficient tuning framework to align large language model with recommendation
Large Language Models (LLMs) have demonstrated remarkable performance across
diverse domains, thereby prompting researchers to explore their potential for use in …
diverse domains, thereby prompting researchers to explore their potential for use in …
A survey on the fairness of recommender systems
Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …
and play an important role in people's daily lives. Since recommendations involve …
Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
Trustworthy graph neural networks: Aspects, methods, and trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …
methods for diverse real-world scenarios, ranging from daily applications such as …
Recbole 2.0: Towards a more up-to-date recommendation library
In order to support the study of recent advances in recommender systems, this paper
presents an extended recommendation library consisting of eight packages for up-to-date …
presents an extended recommendation library consisting of eight packages for up-to-date …
Self-supervised hypergraph transformer for recommender systems
Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative
filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN …
filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN …
A bi-step grounding paradigm for large language models in recommendation systems
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies,
the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a …
the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a …
Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system
The general aim of the recommender system is to provide personalized suggestions to
users, which is opposed to suggesting popular items. However, the normal training …
users, which is opposed to suggesting popular items. However, the normal training …