Little Is Much: Bridging Cross-Platform Behaviors through Overlapped Crowds
Authors: Meng Jiang, Peng Cui, Nicholas Jing Yuan, Xing Xie, Shiqiang Yang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |