Representation Learning-Assisted Click-Through Rate Prediction
Authors: Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Chao Qi, Zhaojie Liu, Yanlong Du
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two large-scale datasets demonstrate that Deep MCP outperforms several state-of-the-art models for CTR prediction. |
| Researcher Affiliation | Industry | Wentao Ouyang, Xiuwu Zhang, Shukui Ren, Chao Qi, Zhaojie Liu and Yanlong Du Alibaba Group {maiwei.oywt, xiuwu.zxw, shukui.rsk, qichao.qc, zhaojie.lzj, yanlong.dyl}@alibaba-inc.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make the implementation code of Deep MCP publicly available1. 1https://github.com/oywtece/deepmcp |
| Open Datasets | Yes | Avito advertising dataset2. This dataset contains a random sample of ad logs from avito.ru, the largest general classified website in Russia. We use the ad logs from 2015-04-28 to 2015-05-18 for training, those on 2015-05-19 for validation, and those on 2015-05-20 for testing. ... 2https://www.kaggle.com/c/avito-context-ad-clicks/data |
| Dataset Splits | Yes | We use the ad logs from 2015-04-28 to 2015-05-18 for training, those on 2015-05-19 for validation, and those on 2015-05-20 for testing. ... We use ad logs of 30 consecutive days during Aug.-Sep. 2018 for training, logs of the next day for validation, and logs of the day after the next day for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | All the methods are implemented in Tensorflow and optimized by the Adagrad algorithm [Duchi et al., 2011]. |
| Experiment Setup | Yes | We set the embedding dimension of each feature as K = 10, because the number of distinct features is huge. We set the number of fully connected layers in neural network-based models as 2, with dimensions 512 and 256. We set the batch size as 128, the context window size as C = 2 and the number of negative ads as Q = 4. The dropout ratio is set to 0.5. |