Deep Session Interest Network for Click-Through Rate Prediction
Authors: Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, Keping Yang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted on both advertising and production recommender datasets and DSIN outperforms other stateof-the-art models on both datasets. |
| Researcher Affiliation | Collaboration | 1Alibaba Group, Hangzhou, China 2Zhejiang University, Hangzhou, China |
| Pseudocode | No | The paper describes the model architecture and its components using mathematical formulations but does not provide a pseudocode block or an algorithm. |
| Open Source Code | Yes | https://github.com/shenweichen/DSIN |
| Open Datasets | Yes | Advertising Dataset2 is a public dataset released by Alimama, an online advertising platform in China. It contains 26 million records from ad display/click logs of 1 million users and 800 thousand ads in 8 days. Logs from 2017-05-06 to 2017-0512 are for training and logs from 2017-05-13 are for testing. https://tianchi.aliyun.com/dataset/dataDetail?dataId=56 |
| Dataset Splits | No | The paper specifies training and testing splits for both datasets but does not mention the use of a separate validation set or a specific validation split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper describes the model architecture and its components but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |