Learning Concise Representations of Users' Influences through Online Behaviors
Authors: Shenghua Liu, Houdong Zheng, Huawei Shen, Xueqi Cheng, Xiangwen Liao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this study, we conduct extensive experiments on real Microblog data, showing that our model with distributed representations achieves better accuracy than the state-of-the-art and pair-wise models, and that learning influences on sentiments benefit performance. |
| Researcher Affiliation | Academia | 1CAS Key Laboratory of Network Data Science & Technology 2 Institute of Computing Technology, Chinese Academy of Sciences 3School of Mathematics and Computer Science, Fuzhou University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | We used real Microblog data crawled from Sina Weibo 1 in which users activity in passing messages is publicly available. The temporal cascades were extracted for all the messages in the data for evaluations. |
| Dataset Splits | Yes | To set up the experiments, the cascades were evenly split into 10 groups. Ten-fold cross-testing is used for our evaluations, alternately training 9 of 10 groups and testing the remaining one. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like |
| Experiment Setup | Yes | The mini-batch size was set at 12 cascades. To solve the non-negative constraints on parameters, Projected Gradient (PG) was used to adjust the gradients. Moreover, since deciding the learning rate is not trivial, we chose Adadelta to adaptively tune the learning rate with an insensitive decay constant ρ = 0.95 as suggested in [Zeiler, 2012]. With testing on vector size of users representations, we choose D = 8 for both efficiency and stable objective value. |