On Organizing Online Soirees with Live Multi-Streaming
Authors: Chih-Ya Shen, C. P. Kankeu Fotsing, De-Nian Yang, Yi-Shin Chen, Wang-Chien Lee
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform a user study on Twitch with 432 participants to validate the need of SDSQ and the usefulness of SDSSel. We also conduct large-scale experiments on real datasets to demonstrate the superiority of the proposed algorithm over several baselines in terms of solution quality and efficiency. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan 2 Institute of Information Science, Academia Sinica, Taipei, Taiwan 3 Department of Computer Science and Engineering, The Pennsylvania State University, PA, USA 4 Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu, Taiwan 5 Social Networks and Human-Centered Computing Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan chihya@cs.nthu.edu.tw, cedricfotsing@yahoo.fr, dnyang@iis.sinica.edu.tw, yishin@cs.nthu.edu.tw, wlee@cse.psu.edu |
| Pseudocode | Yes | Algorithm 1 Social-aware Diverse and Preferred Live Streaming Selection (SDSSel) |
| Open Source Code | No | The paper mentions prototyping a service and using a Chrome plugin for a user study but does not explicitly state that the source code for its proposed methodology (SDSSel) is open-source or provide a link to it. |
| Open Datasets | Yes | We evaluate the effectiveness and the efficiency of SDSSel on three large-scale real datasets, namely Yelp (Yelp 2016), Douban, and Twitch. ...Twitch channels are coupled with a Twitter social network dataset (R. Zafarani and H. Liu 2009). |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, and test splits for the datasets used. It only mentions that "Each result is averaged over 50 samples" which refers to experimental runs, not data partitioning. |
| Hardware Specification | Yes | All algorithms are implemented on an HP DL580 server with 4 Intel Xeon E7-4870 2.4 GHz CPUs and 1 TB RAM. |
| Software Dependencies | No | The paper mentions using "IBM CPLEX" for the Integer Linear Programming formulation, but does not specify a version number for CPLEX or any other software dependencies. |
| Experiment Setup | No | The paper specifies problem parameters 'h' and 'p' but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes, epochs) or specific training configurations for the algorithms implemented or baselines compared. |