Sub-Interest-Aware Representation Uniformity for Recommender System
Authors: Ruijia Ma, Yahong Lian, Chunyao Song
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | 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. |