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..
Life-Stage Modeling by Customer-Manifold Embedding
Authors: Jing-Wen Yang, Yang Yu, Xiao-Peng Zhang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the proposed method, we conduct experiments in a real-world data. Experimental results show that the proposed method can achieve significantly better performance than baseline recommendation approaches. |
| Researcher Affiliation | Collaboration | Jing-Wen Yang , Yang Yu , Xiao-Peng Zhang National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China Tencent Inc., China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The data used in our experiment is provided by Tencent Inc., which consists of customer online shopping history from 7/21/2015 to 2/1/2016 from a real B2C e-commerce system, which servers millions of people everyday. |
| Dataset Splits | No | The paper states 'The data before 1/22/2016 is used for training and the rest is used for testing.' but does not explicitly mention a validation set or specific split percentages/counts for training, validation, and testing. |
| Hardware Specification | No | The paper states 'on the same machine' when discussing time efficiency, but does not provide any specific hardware details such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as library names or frameworks (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x). |
| Experiment Setup | Yes | In the evolutionary similarity computing, the λ is 0.5 and dist equals to 0.5 if two items belongs to the same category, otherwise equals to 5. We adopt only 1 LSTM layer and 2 forward layers. For example, 100-65-2000 denotes that we train a network with 100 hidden units in mini-batch size of 65 for 2000 epochs. |