PREMERE: Meta-Reweighting via Self-Ensembling for Point-of-Interest Recommendation
Authors: Minseok Kim, Hwanjun Song, Doyoung Kim, Kijung Shin, Jae-Gil Lee4164-4171
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments show that replacing a weighting scheme with PREMERE boosts the performance of the state-of-the-art recommender algorithms by 2.36 26.9% on three benchmark datasets. |
| Researcher Affiliation | Academia | Minseok Kim, Hwanjun Song, Doyoung Kim, Kijung Shin, Jae-Gil Lee KAIST, Korea {minseokkim, songhwanjun, doyo09, kijungs, jaegil}@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1 PREMERE Training |
| Open Source Code | Yes | The source code is available at https://github.com/kaist-dmlab/PREMERE. |
| Open Datasets | Yes | We used three popular benchmark datasets, Gowalla (Liu et al. 2017), Foursquare (Yang, Zhang, and Qu 2016), and Yelp (Liu et al. 2017), which are commonly used in the POI recommendation literature (Zhou et al. 2019; Ma et al. 2018). |
| Dataset Splits | No | We randomly selected 80% of check-ins as the training set and used the rest 20% of check-ins as the test set in each dataset. The paper mentions 'meta-data (validation) sets' but states they are 'self-generated' and not a fixed, pre-defined split of the original dataset. |
| Hardware Specification | Yes | Our implementation was written using Py Torch and tested on Nvidia Tesla V100. |
| Software Dependencies | No | The paper states 'Our implementation was written using Py Torch' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | We used Adam (Kingma and Ba 2015) with a learning rate η = 0.001 and a weight decay 0.001. Regarding three hyperparameter of PREMERE, we fixed the moving average weight α = 0.95 and the history length q = 10... the stability threshold ϵ was set to be 0.25 H(x; q)... |