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 [1].
Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences
Authors: Lu Zhang, Zhu Sun, Ziqing Wu, Jie Zhang, Yew Soon Ong, Xinghua Qu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three datasets demonstrate the superiority of CFPRec against state-of-the-arts.We conduct experiments to investigate the following research questions2. (RQ1) Does the proposed CFPRec outperform state-of-the-art baselines? (RQ2) How do different components of CFPRec affect its performance? (RQ3) How do key hyper-parameters of CFPRec affect its performance? |
| Researcher Affiliation | Collaboration | Lu Zhang1 , Zhu Sun2,3 , Ziqing Wu1 , Jie Zhang1 , Yew Soon Ong3,1 and Xinghua Qu4 1Nanyang Technological University, Singapore 2A*STAR Institute of High Performance Computing, Singapore 3A*STAR Centre for Frontier AI Research, Singapore 4Bytedance AI Lab, Singapore |
| Pseudocode | Yes | Algorithm 1: Training of the CFPRec |
| Open Source Code | Yes | Our code is available at https://github.com/wuziqi2/CFPRec |
| Open Datasets | Yes | We adopt three datasets collected from Foursquare [Yang et al., 2016] in three cities, i.e., Singapore (SIN), New York City (NYC) and Phoenix (PHO), as shown in Table 1. |
| Dataset Splits | Yes | We then split the trajectories of each user in the ratio of 8:1:1 based on timestamps, where the earliest 80% is regarded as training set; the second last 10% as validation set and the last 10% as test set. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch' and 'Adam' but does not specify their version numbers or any other specific software dependencies with versions. |
| Experiment Setup | Yes | In particular, the embedding size D is searched in [20, 100] stepped by 20; the learning rate γ and regularization coefficient are searched in {0.0001, 0.001, 0.01, 0.1}. The rest parameters of each method are searched as suggested by the original papers. For CFPRec, we implement it with Pytorch, and Adam is adopted as the optimizer; D = 60/40/60 for SIN, NYC and PHO; the number of iterations are correspondingly set as 25/25/15 for SIN, NYC and PHO; γ = 0.0001; η = 1; the number of Transformer blocks is 1; the number of LSTM layers is 3/2/2 for SIN, NYC and PHO. |