Browsing by Author "Teng, Ya-Wen"

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Blockchain-Based Cross-Organizational Integrated Platform for Issuing and Redeeming Reward Points
(2019-01-01)
Liao, Chia-Hung
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Teng, Ya-Wen
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Yuan, Shyan-Ming
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資訊工程學系
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Department of Computer Science
For the sake of enhancing repurchase rate and customer loyalty, plenty of enterprises have issued reward points. However, as there are more enterprises issuing reward points, customers become less and less interested to collect them. Even though there are companies forming a business alliance to make reward points more attractive, it is usually managed by a central leading company. In other words, there still exists a security concern for a centralized system. In this paper, we propose a blockchain-based cross-organizational integrated platform (BCOIP) for issuing and redeeming reward points. BCOIP provides customers an electronic reward point system that is easier to collect and manage and could circulate in many enterprises. Besides, since BCOIP deploys the smart contract to operate the reward point and make all point transactions stored on the permissioned blockchain, it provides more interoperability and security to enterprises and more credibility for costumers. Moreover, goods providers joining the BCOIP could not only improve the diversity of the reward market but also reach more costumers. Finally, we conduct two experiments to evaluate the efficiency and stability of the BCOIP. Results indicate that points transactions are stable regardless of the number of parallel processes and the other shows the number of nodes has nothing to do with the efficiency of this system.
Optimizing Social-Topic Engagement on Social Network and Knowledge Graph
(2019-01-01)
Teng, Ya-Wen
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Shi, Yishuo
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Tsai, Jui-Yi
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Shuai, Hong-Han
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Tai, Chih-Hua
Existing research on social networks manifests two crucial criteria to improve activity engagement of users: (1) user interests in the activity topics and (2) opportunities of making new friends with some acquaintances. However, current online platforms still involve massive manual selection for activity attendees and contents without proper recommendations. In this paper, therefore, we formulate a new activity organization problem, named Social Knowledge Group Query (SKGQ), to recommend attendees and topic-related contents simultaneously. We prove that SKGQ is NP-hard and design an approximation algorithm, named Social cOntent Knowledge Exploration (SOKE), to jointly choose the activity attendees and topic-related contents according to social-oriented and topic-oriented strategies. Simulation results manifest that the solution acquired by SOKE is close to the optimal solution and outperforms various baselines.