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].
Optimal Route Search with the Coverage of Users' Preferences
Authors: Yifeng Zeng, Xuefeng Chen, Xin Cao, Shengchao Qin, Marc Cavazza, Yanping Xiang
IJCAI 2015 | Venue PDF | 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, EMAIL 2School of Computer Science and Engineering, University of Electronic Science and Technology of China, China, EMAIL 3School of Electronics, Electrical Engineering and Computer Science, Queen s University Belfast, UK, EMAIL |
| 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%. |