Cross-Domain Ranking via Latent Space Learning
Authors: Jie Tang, Wendy Hall
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the effectiveness and efficiency of Bay CDR on large datasets. Experimental results on several real ranking tasks show the effectiveness and efficiency of Bay CDR. |
| Researcher Affiliation | Academia | Jie Tang, Wendy Hall Department of Computer Science and Technology, Tsinghua University Tsinghua National Laboratory for Information Science and Technology (TNList) Electronics and Computer Science, University of Southampton, UK |
| Pseudocode | Yes | Algorithm 1: The learning algorithm for Bay CDR. |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code available or links to a code repository for the methodology described. |
| Open Datasets | Yes | The first (homogeneous) dataset is LETOR 2.0 (Liu et al. 2007), a public dataset for learning to rank research. The second (heterogeneous) dataset is an academic dataset, which is also publicly available. |
| Dataset Splits | Yes | In all the experiments, we conduct five-fold cross-validation for all the methods. |
| Hardware Specification | No | The paper mentions evaluating speedup 'using 1-6 computer nodes' but does not specify any details about the hardware (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using the 'Map-Reduce programming model' but does not list any specific software dependencies or their version numbers (e.g., Python version, library versions). |
| Experiment Setup | No | The paper describes the learning algorithm and the datasets, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or specific optimizer settings used during training. |