Life-Stage Modeling by Customer-Manifold Embedding
Authors: Jing-Wen Yang, Yang Yu, Xiao-Peng Zhang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 yangjw@lamda.nju.edu.cn, yuy@lamda.nju.edu.cn, xpzhang@tencent.com |
| 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. |