Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
Authors: Yichen Wang, Nan Du, Rakshit Trivedi, Le Song
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on diverse real-world datasets demonstrate significant improvements in user behavior prediction compared to state-of-the-arts. We evaluate our framework, COEVOLVE, on synthetic and real-world datasets. |
| Researcher Affiliation | Collaboration | Google Research College of Computing, Georgia Institute of Technology {yichen.wang, rstrivedi}@gatech.edu, dunan@google.com lsong@cc.gatech.edu |
| Pseudocode | No | The paper states: "We provide details in the appendix" for the algorithm, but the appendix is not included in the provided text. Therefore, pseudocode or algorithm blocks are not visible in the given content. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There are no specific repository links or explicit code release statements. |
| Open Datasets | Yes | Our datasets are obtained from three different domains from the TV streaming services (IPTV), the commercial review website (Yelp) and the online media services (Reddit). Yelp is available from Yelp Dataset challenge Round 7. |
| Dataset Splits | Yes | We use all the events up to time T p as the training data, and the rest as testing data, where T is the length of the observation window. We tune hyper-parameters and the latent rank of other baselines using 10-fold cross validation with grid search. We vary the proportion p 2 {0.7, 0.72, 0.74, 0.76, 0.78} and report the averaged results over five runs |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It does not mention any specific hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. No programming languages or libraries with their versions are mentioned. |
| Experiment Setup | Yes | We tune hyper-parameters and the latent rank of other baselines using 10-fold cross validation with grid search. We vary the proportion p 2 {0.7, 0.72, 0.74, 0.76, 0.78} and report the averaged results over five runs |