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..
Inferring Substitutable Products with Deep Network Embedding
Authors: Shijie Zhang, Hongzhi Yin, Qinyong Wang, Tong Chen, Hongxu Chen, Quoc Viet Hung Nguyen
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on seven real-world datasets are conducted, and the results verify that our model outperforms state-of-the-art baselines. |
| Researcher Affiliation | Academia | 1School of Information Technology and Electrical Engineering, The University of Queensland, Australia 2School of Information and Communication Technology, Griffith University, Australia |
| Pseudocode | No | The paper describes the model mathematically and textually but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code open, nor does it provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | We use seven publicly available Amazon product review datasets collected by [Mc Auley et al., 2015] |
| Dataset Splits | No | The paper states "We randomly choose 85% of the labelled substitutable product pairs as the training set, and use the remaining pairs as the test set." It does not explicitly mention a validation set. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names). |
| Experiment Setup | Yes | The hyper-parameters of α, β and γ are also listed in Table 2. For θ and µ in Lss, the optimal values are θ = 0.2 and µ = 0.6, following [Li et al., 2003]. To train SPEM, we set τ, batch size and learning rate as 5, 16 and 0.01 following [Wang et al., 2016]. |