ORCID Profile
0000-0002-4480-169X
Current Organisation
Nanyang Technological University
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Publisher: ACM
Date: 18-07-2023
Publisher: Elsevier BV
Date: 09-2016
Publisher: Association for Computing Machinery (ACM)
Date: 21-08-2023
DOI: 10.1145/3592789
Abstract: Current study on next point-of-interest (POI) recommendation mainly explores user sequential transitions with the fine-grained in idual-user POI check-in trajectories only, which suffers from the severe check-in data sparsity issue. In fact, coarse-grained signals (i.e., region- and global-level check-ins) in such sparse check-ins would also benefit to augment user preference learning. Specifically, our data analysis unveils that user movement exhibits noticeable patterns w.r.t. the regions of visited POIs. Meanwhile, the global all-user check-ins can help reflect sequential regularities shared by the crowd. We are, therefore, inspired to propose the MCMG: a Multi-Channel next POI recommendation framework with Multi-Granularity signals categorized from two orthogonal perspectives, i.e., fine-coarse grained check-ins at either POI/region level or local/global level. The MCMG is equipped with three modules, namely, global user behavior encoder, local multi-channel (i.e., region, category, and POI channels) encoder, and region-aware weighting strategy. Such design enables MCMG to be capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the check-in patterns w.r.t. visited regions. Extensive experiments on four real-world datasets show that our MCMG significantly outperforms state-of-the-art next POI recommendation approaches.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: IEEE
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Publisher: Springer Science and Business Media LLC
Date: 05-08-2015
Publisher: Elsevier BV
Date: 09-2018
Publisher: IEEE
Date: 07-2014
Publisher: Springer Science and Business Media LLC
Date: 04-12-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/ICDM.2011.73
Publisher: IEEE
Date: 04-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 06-2017
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Science and Business Media LLC
Date: 09-10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: Association for Computing Machinery (ACM)
Date: 21-08-2023
DOI: 10.1145/3600226
Abstract: Current session-based recommender systems (SBRSs) mainly focus on maximizing recommendation accuracy, while few studies have been devoted to improve ersity beyond accuracy. Meanwhile, it is unclear how the accuracy-oriented SBRSs perform in terms of ersity. In addition, the asserted “tradeoff” relationship between accuracy and ersity has been increasingly questioned in the literature. Toward the aforementioned issues, we conduct a holistic study to particularly examine the recommendation performance of representative SBRSs w.r.t. both accuracy and ersity, striving for better understanding of the ersity-related issues for SBRSs and providing guidance on designing ersified SBRSs. Particularly, for a fair and thorough comparison, we deliberately select state-of-the-art non-neural, deep neural, and ersified SBRSs by covering more scenarios with appropriate experimental setups, e.g., representative datasets, evaluation metrics, and hyper-parameter optimization technique. The source code can be obtained via yin863/Understanding-Diversity-in-SBRSs . Our empirical results unveil that (1) non- ersified methods can also obtain satisfying performance on ersity, which can even surpass ersified ones, and (2) the relationship between accuracy and ersity is quite complex. Besides the “tradeoff” relationship, they can be positively correlated with each other, that is, having a same-trend (win–win or lose–lose) relationship, which varies across different methods and datasets. Additionally, we further identify three possible influential factors on ersity in SBRSs (i.e., granularity of item categorization, session ersity of datasets, and length of recommendation lists) and offer an intuitive guideline and a potential solution regarding learned item embeddings for more effective session-based recommendation.
Publisher: Elsevier BV
Date: 05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2013
Publisher: ACM
Date: 14-09-2023
Publisher: Elsevier BV
Date: 05-2020
Publisher: ACM
Date: 29-10-2012
Publisher: IEEE
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Springer International Publishing
Date: 2016
Publisher: Inderscience Publishers
Date: 2011
Publisher: Universitas Negeri Semarang
Date: 09-12-2019
DOI: 10.15294/JEJAK.V12I2.21289
Abstract: Regional expansion could be a strength to improve the performance of local governments and subsequently should have a positive impact, such as improving the welfare of the local community. Regional expansion also aims to make governments at regional level to be more focused on escalating potential sectors in their regions. This study analyzes the repositioning of the GRDP contributing sector before and after expansion between two regency regions in 2010-2017, one parental Regency and one area that is separated from its parental. Repositioned sectors show the influence of an area in terms of its wealth of the resources as basis sector. The research method uses Location Quotient (LQ) and Shift Share analysis. Results of this study show that before and after expansion of districts into new regency, agricultural sector has declined in the contribution of GRDP. But after the expansion, the sectors with the best economic performance are the construction, administration and trade & repair sectors. In addition, agricultural sector to some extent has been decreased in the two regions. But at the same time, agricultural sector become leading sector in the new region with slow growth. Implication of this study is that the ision of regions would not create new leading sector if the potential sector in a new region is the leading sector in the older region. Therefore, policy making which ensure basic sectors to have positive proportional shift and differential shift could drive economic development planning in both regions.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: IEEE
Date: 12-2012
DOI: 10.1109/ICDM.2012.77
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Yew-Soon Ong.