Deep Feedback Network for Recommendation
Authors: Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, Leyu Lin
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we conduct both offline and online evaluations on a real-world recommendation system We Chat Top Stories used by millions of users. The significant improvements verify the effectiveness and robustness of DFN. |
| Researcher Affiliation | Industry | We Chat Search Application Department, Tencent, China |
| Pseudocode | No | No pseudocode or algorithm blocks are present. |
| Open Source Code | Yes | The source code is in https://github.com/qqxiaochongqq/DFN. |
| Open Datasets | No | Since there are few large-scale datasets having click, unclick and dislike behaviors, we build a new dataset Multi Feed from a real-world recommendation system We Chat Top Stories after data masking. |
| Dataset Splits | No | Precisely, we randomly collect 448 million user behaviors from 20.3 million users on 3.1 million items, considering the behaviors in the first few days as train set and the rest as test set. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models or memory) are provided for running experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers are provided. |
| Experiment Setup | Yes | In DFN, the max length of all three behavior sequences is 30 and the feature field number is 47. The dimension of each feature embeddings nh = 64, and the dimension of 2-layer MLP in Deep is 32 and 16. In training, we utilize Adam with the batch size to be 64. The weights of click, unclick and dislike losses λc : λu : λd = 1 : 1 : 10. |