SMINet: State-Aware Multi-Aspect Interests Representation Network for Cold-Start Users Recommendation
Authors: Wanjie Tao, Yu Li, Liangyue Li, Zulong Chen, Hong Wen, Peilin Chen, Tingting Liang, Quan Lu8476-8484
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments conducted both offline and online demonstrate the superior performance of the proposed model at user representation, especially for cold-start users, compared with state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1 Alibaba Group,Hangzhou,China 2 Hangzhou Dianzi University,Hangzhou,China |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The details for preprocessing the datasets along with the data and code are released at https://github.com/wanjietao/Fliggy SMINet-AAAI2022 |
| Open Datasets | Yes | We use two datasets1. (1) Fliggy: our proprietary dataset extracted from user s behavior logs at Fliggy, one of the largest OTP in China. ... The details for preprocessing the datasets along with the data and code are released at https://github.com/wanjietao/Fliggy SMINet-AAAI2022. (2) Foursquare: a public dataset that contains check-in data of a user at a particular location at a specific timestamp, along with attribute information of users and locations. |
| Dataset Splits | No | The dataset is further split into training set, test set and validation set. The paper mentions the existence of a validation set but does not provide specific details on the split (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The paper describes the model architecture and loss function but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |