Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation
Authors: Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, Jaewoo Kang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the efficacy of CAPE, we constructed a large-scale POI dataset. In the experimental evaluation, we show that the performance of the existing POI recommendation models can be significantly improved by simply applying CAPE to the models. |
| Researcher Affiliation | Collaboration | 1 Korea University 2 Naver Corporation buru chang@korea.ac.kr, yongqyu@korea.ac.kr, parkdh@korea.ac.kr, seongsoon.kim@navercorp.com, kangj@korea.ac.kr |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations, along with illustrative figures, but does not include formal pseudocode or an algorithm block. |
| Open Source Code | Yes | Our source code implemented in an open library is also publicly available. |
| Open Datasets | Yes | Our newly constructed dataset is available at https://dmis.korea.ac.kr/cape |
| Dataset Splits | Yes | We divide the dataset into training, validation, and test sets using the same method employed in the study of Liu et al. [Liu et al., 2016a]. The most recent check-ins which constitute 20% of the total check-ins of each user are used for the test set. The less recent check-ins which constitute 10% are used for the validation set, and the remaining check-ins which constitute 70% are used for the training set. |
| Hardware Specification | Yes | We report that CAPE, SG-CWARP, and Geo-Teaser took about 3 hours 40 minutes, 3 hours, and 7 hours to be optimized, respectively, on a single GTX Titan X GPU machine. |
| Software Dependencies | No | The paper mentions that the source code is implemented in an "open library" but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We first set the range of POI embedding dimension to 50-450. We then observe the performance changes from increasing the dimension in increments of 50. As shown in Figure 6a, the best performance is obtained when the dimension is set to 300... When we set the context and content window sizes to 1 and 2, respectively, the best performance is obtained. CAPE optimizes the linearly combined lossctx and lossctn using a hyper-parameter α... The POI recommendation models, except ST-RNN, obtain the best performance using CAPE when the hyper-parameter α is 0.4. ST-RNN achieves the best performance when the hyper-parameter is 0.3. |