Weakly Learning to Match Experts in Online Community
Authors: Yujie Qian, Jie Tang, Kan Wu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use two different genres of datasets: QA-Expert and Paper Reviewer, to validate the proposed model. Our experimental results show that the proposed model can significantly outperform (+1.5-10.7% by MAP) the state-of-the-art algorithms for matching users (experts) with community questions. We have also developed an online system to further demonstrate the advantages of the proposed method. |
| Researcher Affiliation | Academia | Yujie Qian , Jie Tang and Kan Wu Tsinghua University Massachusetts Institute of Technology yujieq@csail.mit.edu, jietang@tsinghua.edu.cn, wu-k14@mails.tsinghua.edu.cn |
| Pseudocode | No | The paper contains mathematical formulations and descriptions of algorithms but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code for this paper is publicly available.3 https://www.aminer.cn/match expert |
| Open Datasets | Yes | QA-Expert: this dataset is from an online international QA challenge2. It consists of 87,988 users and 47,656 questions with 43,913 invitations and 9,561 responses. ... 2https://biendata.com/competition/bytecup2016/ |
| Dataset Splits | No | The paper states: "In each dataset, we randomly select 60% as the training data and the remaining as test data." It does not explicitly mention a separate validation dataset or its split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Word2Vec and SVM-Light, but it does not provide specific version numbers for these or any other key software dependencies required for reproducibility. |
| Experiment Setup | No | The paper mentions some empirical settings for baselines (e.g., "set the number of topics to 30" for LDA, "k = 2, b = 0.75" for BM25) and states that the learning algorithm uses stochastic gradient descent and takes "1,000-2,000 iterations to converge". However, it lacks specific hyperparameters for the main Weak FG model, such as learning rates, batch sizes, or detailed optimizer configurations for reproducibility. |