Predicting Activity and Location with Multi-task Context Aware Recurrent Neural Network
Authors: Dongliang Liao, Weiqing Liu, Yuan Zhong, Jing Li, Guowei Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that the proposed model significantly outperforms state-of-the-art approaches. |
| Researcher Affiliation | Collaboration | Dongliang Liao1, Weiqing Liu2, Yuan Zhong3, Jing Li1, Guowei Wang1 1 University of Science and Technology of China 2 Microsoft Research Asia , 3 Facebook Inc. |
| Pseudocode | Yes | Algorithm 1 Alternative Training of MCARNN |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the methodology. |
| Open Datasets | Yes | We evaluate our model on public Foursquare check-in datasets collected from two big cities, New York (NYC) and Tokyo (TKY) [Yang et al., 2015]. |
| Dataset Splits | Yes | In following experiments, for each user, we take the first 80% check-ins as the training set, the latter 10% as the evaluation set, and the last 10% as the validation set for the hyper-parameters study. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions general software like 'Python' or libraries but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The learning rate α starts with 2.00 and decays to one quarter every 4 epochs until it is less than 0.02. We set batch sizes Ba, Bl as 16 and BG as 64. ... Thus we set the hidden layer size as 256. ... Thus the best setting of λ1, λ2 and λ3 is 1, 0.5 and 0.05. |