Understanding Persuasion Cascades in Online Product Rating Systems
Authors: Hong Xie, Yongkun Li, John C.S. Lui5490-5497
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the data from Amazon and Trip Advisor, and show that persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios. We conduct experiments on the Amazon and Trip Advisor datasets, and obtain a number of interesting findings. |
| Researcher Affiliation | Academia | Hong Xie,1,2 Yongkun Li,3 John C.S. Lui2 1College of Computer Science, Chongqing University, China 2Department of Computer Science and Engineering, The Chinese University of Hong Kong 3School of Computer Science and Technology, University of Science and Technology of China |
| Pseudocode | No | The paper describes algorithms (e.g., maximum likelihood algorithm) but does not present them in pseudocode blocks or clearly labeled algorithm boxes. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | The paper states 'We conduct experiments on the data from Amazon and Trip Advisor' but does not provide specific access information (e.g., specific dataset names with DOIs, URLs, or formal citations) to these datasets. |
| Dataset Splits | No | The paper describes using the 'whole rating history' for inference and varying the 'number of ratings i from 1 to 500' for analysis, but does not specify formal training, validation, and test splits for model development and evaluation. |
| Hardware Specification | No | The paper does not explicitly describe any hardware used to run its experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper mentions using the 'interior point algorithmic framework (Wright and Nocedal 1999)' but does not list specific software packages or libraries with version numbers required to replicate the experiments. |
| Experiment Setup | No | The paper specifies model parameters like M={1,2,3,4,5} and N={0,1,...,50}, and a specific form of the persuaded opinion function, but it does not provide typical experimental setup details such as hyperparameter values (e.g., learning rate, batch size, optimizer settings) or system-level training configurations. |