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..
Event Recommendation in Event-Based Social Networks
Authors: Zhi Qiao, Peng Zhang, Chuan Zhou, Yanan Cao, Li Guo, Yanchuan Zhang
AAAI 2014 | Venue PDF | 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. |