Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Contextualized Point-of-Interest Recommendation
Authors: Peng Han, Zhongxiao Li, Yong Liu, Peilin Zhao, Jing Li, Hao Wang, Shuo Shang
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results in our experiments show that our algorithm outperforms all the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1University of Electronic Science and Technology of China 2King Abdullah University of Science and Technology 3Nanyang Technological University 4Tencent AI Lab 5Inception Institute of Artificial Intelligence |
| Pseudocode | Yes | The optimization procedure is summarized in Algorithm 1. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a direct link to a code repository for the described methodology. |
| Open Datasets | Yes | The Gowalla dataset1 contains user check-in data from February 2009 till October 2010 on the Gowalla social network. The dataset contains 18,737 users and 32,510 POIs. The total number of check-ins is 1,278,274. 1http://snap.stanford.edu/data/loc-gowalla.html The Yelp dataset2 contains 860,888 check-ins of 30,887 users and 18,995 POIs. They are from round 7 of the Yelp dataset challenge. 2https://www.yelp.com/dataset/challenge |
| Dataset Splits | Yes | The two experimental datasets are partitioned into training set, tuning set and test set. The check-ins of each user is splitted with ratio 70%, 20% and 10% for training, tuning and testing, respectively. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, etc.) that would be needed for replication. |
| Experiment Setup | Yes | The parameters in our model are sampled as follows: λ1 [0.05, 10], λ2 [0.05, 10], λ4 [10 6, 10 1], the user smoothing factor αuser [0, 1], the POI smoothing factor αpoi [0, 1], Gaussian kernel standard deviation σ [0.1, 10] and the number of clusters for spectral clustering G {20, 50, 100}. |