Device-Cloud Collaborative Correction for On-Device Recommendation
Authors: Tianyu Zhan, Shengyu Zhang, Zheqi Lv, Jieming Zhu, Jiwei Li, Fan Wu, Fei Wu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on multiple datasets show that Co Corr Rec outperforms existing Transformer-based and RNN-based device recommendation models in terms of performance, with fewer parameters and lower FLOPs, thereby achieving a balance between real-time performance and high efficiency. Code is available at https: //github.com/Yuzt-zju/Co Corr Rec. In this section, we conduct extensive experiments on three realworld datasets to answer the following research questions: RQ1: What is the resource consumption required for various basic recommendation models (FLOPs and maximum memory usage)? RQ2: What is the Time delay caused by device-cloud communication of the Co Corr Rec? RQ3: How does our Co Corr Rec perform compared to the baselines under various experimental settings and how do the designs of it affect the performance? |
| Researcher Affiliation | Collaboration | 1 Zhejiang University 2 Huawei Noah s Ark Lab 3 Shanghai Jiao Tong University EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 TTT Block of SCN Input: H Output: Wb, Z Process: Wb, Z XQ, XK, XV θQH, θKH, θV H XQ, XK Ro PE(XQ, XV ), Ro PE(XK, XV ) XKl W0XK XV Zl σ ( XKl) σ is the derivative of sigmod function W0l Zl(XK)T Wb W0 W0l b b0 mask( Zl) attn mask(XT KXQ) Z W0XQ Zl attn + b return Wb, Z |
| Open Source Code | Yes | Code is available at https: //github.com/Yuzt-zju/Co Corr Rec. |
| Open Datasets | Yes | We conduct experiments on three publicly available datasets in different scenarios. Amazon-beauty and Amazonelectronic are from an e-commerce platform Amazon1, which https://jmcauley.ucsd.edu/data/amazon/ covers various product categories such as books, electronics, home goods, and more. We also evaluated on Yelp2 which is a representative business dataset containing user reviews for different restaurants. 2https://www.yelp.com/dataset/ |
| Dataset Splits | No | For all datasets, we first sort all in-interactions chronologically according to the timestamps. And then discard users and items with interactions 10. All user-item pairs in the dataset are treated as positive samples. In the training and test sets, the user-item pairs that do not exist in the dataset are sampled at 1:4 and 1:100, respectively, as negative samples. |
| Hardware Specification | Yes | All models are trained using a single RTX-4090 GPU. |
| Software Dependencies | No | Optimizer Adam W |
| Experiment Setup | Yes | We tune the learning rates of all models in {1e 3, 2e 3, 5e 3, 1e 2} and select the best performance. As to model-specific hyper-parameters, we select default parameters from open-source code. Additionally, unless otherwise specified, the length of the user sequence is 10. All models are trained using a single RTX-4090 GPU. Other hyperparameters of training are shown in table ?? and the number of head is for transformer-based models. Dataset Model Hyperparameter Setting Beauty Electronic Yelp DIN GRU4Rec SASRec BERT4Rec Mamba4Rec Co Corr Rec GPU RTX-4090 GPU(24G) Optimizer Adam W Learning rate {1e 3, 2e 3, 5e 3, 1e 2} Batch size 1024 Sequence length 10 Dimension of Embedding 1 64 Number of Head 4 Table 3: Hyperparameters of training. |