Optimal Region Search with Submodular Maximization
Authors: Xuefeng Chen, Xin Cao, Yifeng Zeng, Yixiang Fang, Bin Yao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on two applications using three real-world datasets. The results demonstrate that our algorithms can achieve high-quality solutions and are faster than a state-of-the-art method by orders of magnitude. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, University of New South Wales, Australia, 2 Department of Computer & Information Sciences, Northumbria University, UK, 3 Department of Computer Science and Engineering, Shanghai Jiaotong University, China |
| Pseudocode | Yes | Algorithm 1: App ORS Algorithm |
| Open Source Code | No | The paper does not include an unambiguous statement where the authors state they are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We use two real-world datasets SG and AS crawled from Four Square (also used in the work [Zeng et al., 2015]), in which SG has 189,306 check-ins made by 2,321 users at 5,412 locations in Singapore, and AS contains 201,525 check-ins made by 4,630 users at 6,176 locations in Austin. [...] We use the road network in California (CA) from a public website1. We then utilized the Foursquare APIs to fill in the missing keywords for nodes (categories of locations)2. CA contains 21,048 nodes and 22,830 edges [Li et al., 2005]. |
| Dataset Splits | No | The paper mentions using real-world datasets for experiments but does not provide specific details on how the data was split into training, validation, or test sets, nor does it reference predefined splits. |
| Hardware Specification | Yes | We implement all the algorithms in JAVA on Windows 10, and run on a server with an Intel(R)Xeon(R) W-2155 3.3GHz CPU and 256 GB memory. |
| Software Dependencies | No | The paper states that algorithms are implemented 'in JAVA on Windows 10' but does not provide specific version numbers for Java or any other software libraries or dependencies used. |
| Experiment Setup | Yes | We compare three proposed algorithms with GCBAll in terms of efficiency (the run time) and effectiveness (the objective score) by varying Δ from 20km to 60km. [...] Next, we run the three algorithms on SY by varying the number of nodes from 10,000 to 30,000, and set Δ = 60km. |