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..
Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds
Authors: Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, Shiqiang Yang
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across two real social networks show that XPTRANS significantly outperforms the state-of-the-art. |
| Researcher Affiliation | Collaboration | Meng Jiang, Peng Cui Tsinghua University Nicholas Jing Yuan, Xing Xie Microsoft Research Asia Shiqiang Yang Tsinghua University |
| Pseudocode | Yes | Algorithm 1 XPTRANS: Semi-supervised transfer learning for cross-platform behavior prediction |
| Open Source Code | No | No explicit statement or link providing concrete access to the source code for the methodology described in this paper was found. |
| Open Datasets | No | We use the Sina Weibo (tag, tweet entity) and Douban (book, movie, music) data sets in our experiments. We identified the overlapped users with their log-in accounts. Table 1 lists the data statistics. |
| Dataset Splits | No | We set the percentage of training behavioral entries in R(P ) by non-overlapping users as 70%, the percentage of auxiliary behavioral entries in R(Q) by non-overlapping users as 70%, and the other two parameters: α(P Q) R [0, 100%]: the percentage of overlapping behavioral entries in R(P ) and R(Q); α(P Q) U [0, 100%]: the percentage of the most active overlapping users in R(P ) and R(Q). |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library or solver names with version numbers) were mentioned. |
| Experiment Setup | No | The paper defines parameters like λ and μ in the objective function, but it does not specify concrete numerical values for these hyperparameters or other training configurations like learning rates, batch sizes, or epochs. |