Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System
Authors: Hong Wen, Jing Zhang, Quan Lin, Keping Yang, Pipei Huang338-345
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on off-line data set and online deployment demonstrate the effectiveness of the proposed methods. |
| Researcher Affiliation | Collaboration | 1 Alibaba Group 2 Hangzhou Dianzi University 3 University of Technology Sydney |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The off-line benchmark data set was constructed from the real click and purchase logs of our recommendation module in several consecutive days of December, 2017. |
| Dataset Splits | Yes | When preparing off-line benchmark data set, we divided the whole benchmark data into three disjoint parts according to the id, i.e., 40, 20 and 40 percent of the whole benchmark data for training data, validation data and test data respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Hyper-parameters settings We choose the hyper-parameters of the proposed methods according to the AUC metric on the validation set. And the main hyper-parameters of the proposed methods we used in all the following concern experiments are shown in Tab. ??. |