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..
Sub-Interest-Aware Representation Uniformity for Recommender System
Authors: Ruijia Ma, Yahong Lian, Chunyao Song
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on four datasets demonstrate that SIURec achieves superior learning of uniformity (with an average improvement of 4.26% in accuracy compared to eleven SOTA methods) and exhibits robustness across different hyperparameter settings. [...] 5 Experiments In this section, we evaluate SIURec on different datasets to answer following questions: RQ1: How does SIURec perform compared to other competitive methods under different experimental settings? RQ2: How does each component of SIURec contribute to performance enhancement? RQ3: How robust is SIURec under various parameter settings? |
| Researcher Affiliation | Academia | Ruijia Ma, Yahong Lian, Chunyao Song* College of Computer Science, TJ Key Lab of NDST, DISSec, TMCC, TBI Center, Nankai University, Tianjin, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical equations and textual explanations, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/xderui/SIURec |
| Open Datasets | Yes | Datasets. We select four commonly used public benchmark datasets in our experiments: Movie Lens-1M (ML1M), Gowalla, Amazon-Beauty (Beauty) and Amazon-Book (Book). The dataset statistics are shown in Table 1. |
| Dataset Splits | Yes | For each dataset, we group them by user and divide them into 8:1:1 ratios for training, validation, and testing. |
| Hardware Specification | Yes | All experiments are implemented on an Intel(R) Xeon(R) Silver 4110 @ 2.10GHz CPU and an NVIDIA Ge Force RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions Light GCN as a base model and other comparative methods, but it does not specify the versions of the programming languages or libraries used for the implementation of SIURec. |
| Experiment Setup | Yes | Regard to SIURec, we set the initial values αB = 1, αU = 1, and αR = 2.5 10 5. For each baseline, we set the parameters following the suggestions from each individual s work. |