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..
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention
Authors: Yehjin Shin, Jeongwhan Choi, Hyowon Wi, Noseong Park
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our proposed approach through extensive experiments on 6 benchmark datasets. The experimental results demonstrate that our model outperforms 7 baseline methods in terms of recommendation performance. |
| Researcher Affiliation | Academia | Yonsei University, Seoul, South Korea EMAIL |
| Pseudocode | No | The paper describes its proposed model architecture and process in text and through a diagram (Figure 4), but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/yehjin-shin/BSARec. |
| Open Datasets | Yes | Datasets We evaluate our model on 6 SR datasets where the sparsity and domain varies: i,ii,iii) Amazon Beauty, Sports, Toys (Mc Auley et al. 2015), iv) Yelp, v) ML1M (Harper and Konstan 2015), and vi) Last FM. |
| Dataset Splits | No | The paper defines how items are selected for next-item prediction but does not provide specific details on the train, validation, and test dataset splits (e.g., percentages or sample counts) within the main text. It mentions data pre-processing and refers to an Appendix for best hyperparameters, implying typical splits are used, but they are not explicitly defined here. |
| Hardware Specification | Yes | Our method is implemented in Py Torch on an NVIDIA RTX 3090 with 16 GB memory. |
| Software Dependencies | No | The paper states that the method is "implemented in Py Torch" but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We conduct experiments under the following hyperparameters: the coefficient α is in t0.1, 0.3, 0.5, 0.7, 0.9u, and c is chosen from t1, 3, 5, 7, 9u. The number of BSA blocks L is set to 2, and the number of heads in Transformer h is in t1, 2, 4u. The dimension of D is set to 64, and the maximum sequence length N is set to 50. For training, the Adam optimizer is optimized with a learning rate in {5 ˆ 10 4, 1 ˆ 10 3}, and the batch size is set to 256. |