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..
Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data
Authors: Yatong Sun, Bin Wang, Zhu Sun, Xiaochun Yang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four real-world datasets demonstrate the superiority of our proposed BERD. Additionally, detailed ablation study further confirms the effectiveness of each module of BERD. |
| Researcher Affiliation | Academia | 1Northeastern University, China 2Macquarie University, Australia |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link for open-source code. |
| Open Datasets | Yes | Datasets. We adopt four datasets varying w.r.t. domain, size, sparsity level, and ratio of unreliable data, as shown in Table 2. ML-1M [Harper and Konstan, 2015] is a popular dataset for movie recommendation. Steam [Kang and Mc Auley, 2018] is a game recommendation benchmark collected from Steam. CD and Elect are product review datasets crawled from Amazon [Mc Auley and Leskovec, 2013] for cd and electronics, respectively. |
| Dataset Splits | Yes | To be more specific, for each user, we split the last two interactions (instances) into validation and test sets, respectively, while the rest are used for training. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Parameter Settings. We adopt Xavier [Glorot and Bengio, 2010] initializer and Adam [Kingma and Ba, 2015] optimizer with d = 50; the learning rate η = 0.01 with batch size of 8192; the weight of sampled loss λ = 0.01 and uncertainty margin γ = 1; the number of propagation layers K = 2; the input length L = 5; the sample size Z = 4; the filter ratio α = 0.05 for Steam and CD, and α = 0.1 for ML-1M and Elect; the head number of self-attention is set to 2. |