Retaining Data from Streams of Social Platforms with Minimal Regret
Authors: Nguyen Thanh Tam, Matthias Weidlich, Duong Chi Thang, Hongzhi Yin, Nguyen Quoc Viet Hung
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on large-scale real-world datasets illustrate the feasibility of our approach in terms of both, runtime and information quality. |
| Researcher Affiliation | Academia | 1 Ecole Polytechnique F ed erale de Lausanne, 2 Humboldt-Universit at zu Berlin, 3 The University of Queensland, 4 Griffith University |
| Pseudocode | Yes | Algorithm 1: A Progressive Retaining Algorithm |
| Open Source Code | No | The paper does not provide any statement about releasing the source code for the methodology or a link to a code repository. |
| Open Datasets | No | The paper mentions extracting datasets using the Twitter Streaming API ("We extracted datasets using the Twitter Streaming API."), but it does not provide a direct link, DOI, specific repository name, or a formal citation to make these collected datasets publicly available. |
| Dataset Splits | No | The paper describes how portions of the data were used for evaluation (e.g., "select 100K items E from the original datasets and construct a set of retained items S as the k = 1% oldest items in E. We stream E and learn model parameters online"), but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | All results have been obtained on an Intel i7 3.8GHz system (4 cores, 16GB RAM). |
| Software Dependencies | No | The paper mentions using "existing frameworks [Zhuang et al., 2016]" for social features but does not provide specific software names with version numbers (e.g., Python, PyTorch, TensorFlow, specific libraries or solvers with versions) used for their implementation. |
| Experiment Setup | Yes | Following [Hoffman et al., 2013], we vary the forget rate in (0.5, 1], choose a stable window size = 10 and report average values. |