Diversifying Personalized Recommendation with User-session Context
Authors: Liang Hu, Longbing Cao, Shoujin Wang, Guandong Xu, Jian Cao, Zhiping Gu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on a real-world e-commerce dataset show the superiority of our model over the state-of-the-art methods. |
| Researcher Affiliation | Academia | University of Technology, Sydney Shanghai Jiao Tong University *Shanghai Technical Institute of Electronics & Information |
| Pseudocode | Yes | Algorithm 1 A GPU-based SGD Optimizer for SWIWO |
| Open Source Code | Yes | The MATLAB implementation1 of Algorithm 1 is provided online for more details. 1https://github.com/rainmilk/ijcai17swiwo |
| Open Datasets | Yes | We used the IJCAI-15 competition dataset2 for our experiments. This real-world dataset was collected from Tmall.com... 2https://tianchi.shuju.aliyun.com/datalab/dataSet.htm?id=1 |
| Dataset Splits | No | From the six-month shopping logs, we randomly held out 20% of the sessions from the last 30 days for testing, and the remaining data are used for training. It does not explicitly mention a separate 'validation' split, only training and testing. |
| Hardware Specification | No | Not found. The paper mentions a 'GPU-based' optimizer but provides no specific details about the hardware (e.g., specific GPU models, CPUs, memory). |
| Software Dependencies | No | The MATLAB implementation1 of Algorithm 1 is provided online for more details. Our experimental results were obtained using Adam. No version numbers are specified for MATLAB or the Adam optimizer. |
| Experiment Setup | Yes | We set 50 units for the context embeddings and 10 units for the user embeddings when training SWIWO-I and SWIWO models. |