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..
Recommendation with Social Dimensions
Authors: Jiliang Tang, Suhang Wang, Xia Hu, Dawei Yin, Yingzhou Bi, Yi Chang, Huan Liu
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world data sets demonstrate the effectiveness of the proposed framework. |
| Researcher Affiliation | Collaboration | Texas A&M University, EMAIL Yahoo Labs, EMAIL Arizona State University, EMAIL Guangxi Teachers Education University, EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. The paper describes the mathematical equations for the model and updates. |
| Open Source Code | No | No concrete access to source code for the methodology was provided. The links given are for datasets only. |
| Open Datasets | Yes | We collect two datasets to evaluate our proposed recommender system, i.e., Epinions and Ciao1, and these two datasets are publicly available via the homepage of the first author 2. 1http://www.ciao.co.uk/ 2http://www.jiliang.xyz/trust.html |
| Dataset Splits | No | For each dataset, we choose x% as the training set to learn parameters and the remaining 1 x% as the testing set where x is varied as {45, 65, 85}. The paper mentions training and testing sets, but does not explicitly detail a separate validation set or its split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or computing cluster specifications) were provided for the experimental setup. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. The paper describes the mathematical framework and algorithms but does not list programming languages, libraries, or solvers with versions. |
| Experiment Setup | Yes | For So Dim Rec, we set {K = 20, c = 100, λ1 = 5, λ2 = 100} and {K = 30, c = 500, λ1 = 10, λ2 = 100} for Ciao and Epinions, respectively. α is empirically set to 0.1. |