Optimal Route Search with the Coverage of Users' Preferences
Authors: Yifeng Zeng, Xuefeng Chen, Xin Cao, Shengchao Qin, Marc Cavazza, Yanping Xiang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments conducted on real-world datasets demonstrate both the efficiency and accuracy of our proposed algorithms. |
| Researcher Affiliation | Academia | 1School of Computing, Teesside University, UK, {y.zeng, s.qin, m.o.cavazza}@tees.ac.uk 2School of Computer Science and Engineering, University of Electronic Science and Technology of China, China, {cxflovechina, xiangyanping}@gmail.com 3School of Electronics, Electrical Engineering and Computer Science, Queen s University Belfast, UK, x.cao@qub.ac.uk |
| Pseudocode | Yes | Algorithm 1: A* Algorithm for ORS-KC |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | Datasets. We use two real-world datasets. One has been collected from Foursquare which was made in Singapore (SG) between Aug. 2010 and Jul. 2011 [Yuan et al., 2013], and another one is from Gowalla which was made in Austin (AS) between Nov. 2009 and Oct. 2010 [Cho et al., 2011]. |
| Dataset Splits | No | The paper mentions using 'real-world datasets' but does not specify any train/validation/test splits, percentages, sample counts, or refer to predefined splits. |
| Hardware Specification | Yes | implement methods in JAVA and conduct experiments on a Windows PC with a 4-core Intel i7-870 2.93GHz CPU and 16 GB memory. |
| Software Dependencies | No | The paper mentions implementation in 'JAVA' but does not specify a version number for Java or any other software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | We denoted the route found by the WA* algorithm as Rsw, if ω = 1, the WA* algorithm gets the optimal route Rop, then we use the ratio KC(Rsw) KC(Rop) to measure the precision of the WA* algorithm. Fig. 3 shows the effect of ω with = 15 kilometers on solving the ORS-KC problem. With the increase of ω, the precision improves while the efficiency drops (longer run time). In particular, the change of precision and run time is notable from ω = 0.3 to ω = 0.4, which indicates that 0.4hn( ) = 0.4 KC(Lmg) 1 1/ e is close to the upper bound of the marginal keyword coverage hn(Rn t|Rs n). As (1 1/ e) approximates 0.4, KC(Lmg) is near the upper bound of hn(Rn t|Rs n) in most cases. We thus set the weighting coefficient to 0.2, which drastically reduces computation time while limiting solution quality deterioration to 5%. |