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Found 16 papers, 7 papers with code
Sequence-level Semantic Representation Fusion for Recommender Systems
The core idea of our approach is to conduct a sequence-level semantic fusion approach by better integrating global contexts.
Prompting Large Language Models for Recommender Systems: A Comprehensive Framework and Empirical Analysis
As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs.
Distillation is All You Need for Practically Using Different Pre-trained Recommendation Models
Pre-trained recommendation models (PRMs) have attracted widespread attention recently.
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
The optimized agents can also propagate their preferences to other agents in subsequent interactions, implicitly capturing the collaborative filtering idea.
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions.
Towards Complex Real-World Safety Factory Inspection: A High-Quality Dataset for Safety Clothing and Helmet Detection
However, limited availability of high-quality datasets has hindered the development of deep learning methods for safety clothing and helmet detection.
Large Language Models are Zero-Shot Rankers for Recommender Systems
Recently, large language models (LLMs) (e. g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs.
Uncertainty-driven Trajectory Truncation for Data Augmentation in Offline Reinforcement Learning
To empirically show the advantages of TATU, we first combine it with two classical model-based offline RL algorithms, MOPO and COMBO.
Voxel or Pillar: Exploring Efficient Point Cloud Representation for 3D Object Detection
Efficient representation of point clouds is fundamental for LiDAR-based 3D object detection.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection
Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors.
Recent Advances in RecBole: Extensions with more Practical Considerations
In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.
Review on quasi-2D square planar nickelates
In strongly correlated materials, lattice, charge, spin and orbital degrees of freedom interact with each other, leading to emergent physical properties such as superconductivity, colossal magnetic resistance and metal-insulator transition.
Superconductivity Materials Science Strongly Correlated Electrons
Neural Architecture Search for Joint Human Parsing and Pose Estimation
With the spirit of NAS, we propose to search for an efficient network architecture (NPPNet) to tackle two tasks at the same time.
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning
Second, the extra unlabeled samples are employed to transfer the knowledge from base classes to novel classes through contrastive learning.
