Event Recommendation in Event-Based Social Networks
Authors: Zhi Qiao, Peng Zhang, Chuan Zhou, Yanan Cao, Li Guo, Yanchuan Zhang
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several real-world datasets demonstrate the utility of our method. Experiments carried out on several real data sets verify the effectiveness of our proposed model. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences 2Institute of Computing Technology and the University of the Chinese Academy of Sciences 3Victoria University, Melbourne, Australia |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. The paper describes methods using mathematical formulas and textual descriptions, but not structured pseudocode. |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology or a link to a code repository was found. |
| Open Datasets | Yes | We got the five data sets as in Table 1 for the five American cities in Meetup by extracting them from the data sets published by (Zhang and et al. 2013). |
| Dataset Splits | No | The paper states: 'For all the data sets, we randomly split them with 80% into the training sets and 20% into the test sets.' It does not explicitly mention a separate validation split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) used for running the experiments were provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or programming languages with versions) were mentioned in the paper. |
| Experiment Setup | No | The paper mentions 'stochastic gradient descent' for parameter learning and a sampling strategy ('randomly sample 10 events users have not joined for each negative events and and 1 event joined by the users for each positive event'). However, it does not provide specific hyperparameter values like learning rate, batch size, or number of epochs. |