Modeling Status Theory in Trust Prediction
Authors: Ying Wang, Xin Wang, Jiliang Tang, Wanli Zuo, Guoyong Cai
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. |
| Researcher Affiliation | Academia | 1College of Computer Science and Technology, Jilin University, Changchun 130012, China 2Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, China 3College of Computer Science and Engineering, Arizona State University, Tempe, AZ 85281, USA 4School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China 5Guangxi Key Lab of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China |
| Pseudocode | Yes | Algorithm 1 The Proposed Framework s Trust with Status Theory. |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available, nor does it include a link to a code repository. |
| Open Datasets | Yes | To study the problem of trust prediction with status theory, we collect two publically available datasets, i.e., Epinions and Ciao (Tang et al. 2013). |
| Dataset Splits | Yes | All parameters of trust predictors are determined via cross-validation. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or specific server configurations) used for running the experiments. |
| Software Dependencies | No | The paper mentions mathematical frameworks and algorithms but does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For s Trust, we set λ to 0.7 and 0.5 for Epinions and Ciao, respectively. We empirically set α = 0.1 and d = 100. |