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..
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec
Authors: Yu-Hsuan Huang, Ling Lo, Hongxia Xie, Hong-Han Shuai, Wen-Huang Cheng
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results demonstrate our state-of-the-art performance across four benchmark datasets, with an average improvement of 6.16% across all metrics. |
| Researcher Affiliation | Academia | 1National Yang Ming Chiao Tung University, 2Jilin University, 3National Taiwan University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical formulations and descriptive text, but does not include a distinct pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any concrete statement about the availability of source code, nor does it include a link to a code repository. |
| Open Datasets | Yes | We use the Amazon dataset with three categories: Sports, Beauty, Toys, and the Yelp dataset. |
| Dataset Splits | No | The paper describes how subsequences are constructed from user interaction sequences for training, but it does not provide specific train/test/validation split percentages or sample counts for the datasets used in the experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various hyperparameters and experimental settings but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | Parameter tuning is meticulously carried out; ฯ2 is varied within the set {8, 10}, while ฯ1 was fixed at 1, ยต at 0.1, and m at 0.2. The parameters ฮณ and ฮป are each tuned over {0.1, 0.2, 0.3, 0.4, 0.5}. We incorporate enduring hard negatives into the training process after a 20-epoch warm-up period. All experiments were conducted three times, and results were averaged for comparison. |