A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
A comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
Making language models better reasoners with step-aware verifier
Few-shot learning is a challenging task that requires language models to generalize from
limited examples. Large language models like GPT-3 and PaLM have made impressive …
limited examples. Large language models like GPT-3 and PaLM have made impressive …
Active prompting with chain-of-thought for large language models
The increasing scale of large language models (LLMs) brings emergent abilities to various
complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is …
complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is …
Cumulative reasoning with large language models
While language models are powerful and versatile, they often fail to address highly complex
problems. This is because solving complex problems requires deliberate thinking, which has …
problems. This is because solving complex problems requires deliberate thinking, which has …
Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network
Session-based recommendation (SBR) aims to predict the user's next action based on short
and dynamic sessions. Recently, there has been an increasing interest in utilizing various …
and dynamic sessions. Recently, there has been an increasing interest in utilizing various …
Reinforcement subgraph reasoning for fake news detection
The wide spread of fake news has caused serious societal issues. We propose a subgraph
reasoning paradigm for fake news detection, which provides a crystal type of explainability …
reasoning paradigm for fake news detection, which provides a crystal type of explainability …
Semi-offline reinforcement learning for optimized text generation
Existing reinforcement learning (RL) mainly utilize online or offline settings. The online
methods explore the environment with expensive time cost, and the offline methods …
methods explore the environment with expensive time cost, and the offline methods …
A survey on reinforcement learning for recommender systems
Recommender systems have been widely applied in different real-life scenarios to help us
find useful information. In particular, reinforcement learning (RL)-based recommender …
find useful information. In particular, reinforcement learning (RL)-based recommender …
Predicting information pathways across online communities
The problem of community-level information pathway prediction (CLIPP) aims at predicting
the transmission trajectory of content across online communities. A successful solution to …
the transmission trajectory of content across online communities. A successful solution to …