Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Path-Enhanced Contrastive Learning for Recommendation
Authors: Haoran Sun, Fei Xiong, Yuanzhe Hu, Liang Wang
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world datasets demonstrate the effectiveness of our model. |
| Researcher Affiliation | Academia | Haoran Sun Beijing Jiaotong University EMAIL Fei Xiong Beijing Jiaotong University EMAIL Yuanzhe Hu Institute of Software Chinese Academy of Sciences EMAIL Liang Wang Northwestern Polytechnical University EMAIL |
| Pseudocode | Yes | Algorithm 1: PECL Framework Training Algorithm |
| Open Source Code | Yes | Answer: [Yes] Justification: Yes, all experimental settings, codes, pesudo code and datasets are provided in paper and github. |
| Open Datasets | Yes | Our experiments utilize three publicly available, real-world datasets ML-1M, Ciao and Amazon that offer rich information, including both user-item ratings and the corresponding timestamps of user interactions. |
| Dataset Splits | Yes | Each dataset is divided into training and test sets by randomly selecting 80% of the rating entries for training purposes, while the leftover 20% is reserved to evaluate the model s performance during testing. |
| Hardware Specification | Yes | All experiments we conducted are performed with Intel(R) Xeon(R) Bronze 3204 CPU @ 1.90GHz, Tesla A100, and 256GB memory, running the Ubuntu 20.04.4 LTS operating system. |
| Software Dependencies | No | The paper mentions the operating system (Ubuntu 20.04.4 LTS), but does not specify programming languages or library versions like Python, PyTorch, or CUDA, which are common for such research. Therefore, it does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | To assess the effectiveness of our recommendation approach, we rely on two widely adopted evaluation metrics: Recall@K and NDCG@K. In our experiment, K is set to 10 and 20. ... To comprehensively evaluate the impact of hyperparameters on PECL performance, we performed a series of controlled experiments by varying key hyperparameters. Specifically, we focus on three crucial hyperparameters: α (Eq.6), β (Eq.9), and τ (Eqs.7, 14). |