Interest Inference via Structure-Constrained Multi-Source Multi-Task Learning
Authors: Xuemeng Song, Liqiang Nie, Luming Zhang, Maofu Liu, Tat-Seng Chua
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on a real-world dataset validated our scheme. |
| Researcher Affiliation | Academia | National University of Singapore, Wuhan University of Science and Technology {sxmustc, nieliqiang, zglumg}@gmail.com, liumaofu@wust.edu.cn, chuats@comp.nus.edu.sg |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: "We have released our compiled dataset... The compiled dataset is currently publicly accessible via: http://msmt.farbox.com/." This refers to the dataset, not the source code for the proposed methodology. |
| Open Datasets | Yes | We have released our compiled dataset2, which will facilitate other researchers to repeat our approach and to comparatively verify their own ideas. The compiled dataset is currently publicly accessible via: http://msmt.farbox.com/. |
| Dataset Splits | Yes | Experimental results reported in this work are the average values over 10-fold cross validation. |
| Hardware Specification | Yes | All the experiments were conducted over a server equipped with Intel(R) Xeon(R) CPU X5650 at 2.67GHZ on 48GB RAM, 24 cores and 64-bit Cent OS 5.4 operating system. |
| Software Dependencies | No | The paper mentions software like LIBSVM, Boiler Pipe, and LDA but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We adopted the grid search strategy to determine the optimal values for the regularization parameters among the values {10r : r { 12, , 1}}. Experimental results reported in this work are the average values over 10-fold cross validation. Noticeably, we tuned the K in S@K and P@K from 1 to 10 and reported the optimal performance for each fold. |