Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |