Updated on 2022/09/25

写真a

 
BAO, Siya
 
Affiliation
Faculty of Science and Engineering, School of Fundamental Science and Engineering
Job title
Assistant Professor(without tenure)

Education

  • 2017.04
    -
    2020.03

    Waseda University   Graduate School of Fundamental Science and Engineering  

  • 2015.09
    -
    2017.03

    Waseda University   Graduate School of Fundamental Science and Engineering  

  • 2011.09
    -
    2015.09

    Waseda University   School of Fundamental Science and Engineering  

Research Experience

  • 2020.04
    -
    Now

    Waseda University   Faculty of Science and Engineering   Assistant Professor

Professional Memberships

  •  
     
     

    情報処理学会

  •  
     
     

    電子情報通信学会

  •  
     
     

    IEEE

 

Research Areas

  • High performance computing

  • Database

Research Interests

  • 量子計算

  • 地理空間情報処理

  • テキストマイニング

Papers

  • An Approach to the Vehicle Routing Problem with Balanced Pick-up Using Ising Machines

    Siya Bao, Masashi Tawada, Shu Tanaka, Nozomu Togawa

    2021 International Symposium on VLSI Design, Automation and Test (VLSI-DAT)    2021.04  [Refereed]

    DOI

  • Multi-day Travel Planning Using Ising Machines for Real-world Applications.

    Siya Bao, Masashi Tawada, Shu Tanaka, Nozomu Togawa

    24th IEEE International Intelligent Transportation Systems Conference(ITSC)     3704 - 3709  2021  [Refereed]

    DOI

  • Document-level sentiment classification in japanese by stem-based segmentation with category and data-source information

    Siya Bao, Nozomu Togawa

    Proceedings - 14th IEEE International Conference on Semantic Computing, ICSC 2020     311 - 314  2020.02  [Refereed]

     View Summary

    © 2020 IEEE. Existing studies focus on text information while ignoring category and data source information, both of which are verified to be important in interpreting sentiments in travel comments in this paper. Furthermore, the unique linguistic characteristics of Japanese cause difficulty in applying the conventional token-based word segmentation methods to Japanese comments directly. In this paper, we propose a method of stem-based segmentation based on Japanese linguistic characteristics and incorporate it with category and data source information into a hierarchical network model for document-level sentiment classification. Empirical results of our proposed model outperform existing models on a real-world dataset.

    DOI

  • A travel decision support algorithm: Landmark activity extraction from japanese travel comments

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    Studies in Computational Intelligence   849   109 - 123  2020  [Refereed]

     View Summary

    © Springer Nature Switzerland AG 2020. To help people smoothly and efficiently make travel decisions, we utilize the advantages of travel comments posted by thousands of other travelers. In this paper, we analyze the feasibility of exploring landmark activity queries and representative examples from Japanese travel comments. Contributions in this paper include a framework for extracting activity concerned keywords and queries, quantifying the relationship between landmark activities and comment contents. An evaluation of activity-example extraction is conducted in two case studies through 18,939 travel comments.

    DOI

  • Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018     3628 - 3637  2019.01  [Refereed]

     View Summary

    © 2018 IEEE. Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.

    DOI

  • Personalized Landmark Recommendation for Language-Specific Users by Open Data Mining

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    Studies in Computational Intelligence   791   107 - 121  2019  [Refereed]

     View Summary

    © 2019, Springer Nature Switzerland AG. This paper proposes a personalized landmark recommendation algorithm aiming at exploring new sights into the determinants of landmark satisfaction prediction. We gather 1,219,048 user-generated comments in Tokyo, Shanghai and New York from four travel websites. We find that users have diverse satisfaction on landmarks those findings, we propose an effective algorithm for personalize landmark satisfaction prediction. Our algorithm provides the top-6 landmarks with the highest satisfaction to users for a one-day trip plan our proposed algorithm has better performances than previous studies from the viewpoints of landmark recommendation and landmark satisfaction prediction.

    DOI

  • Personalized landmark recommendation algorithm based on language-specific satisfaction prediction using heterogeneous open data sources

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    Proceedings - 2018 10th International Conference on Computational Intelligence and Communication Networks, CICN 2018     70 - 76  2018.08  [Refereed]

     View Summary

    © 2018 IEEE. This paper proposes a personalized landmark recommendation algorithm based on the prediction of users' satisfaction on landmarks. We have accumulated 270,239 user-generated comments from travel websites of Ctrip, Jaran and TripAdvisor for 196 landmarks in Tokyo, Japan. We find that users do have different satisfaction on landmarks depending on their commonly used languages and travel websites. Then we establish a database for landmarks with abundant and accurate landmark type and landmark satisfaction information. Finally, we propose an effective personalized landmark satisfaction prediction algorithm based on users' landmark type, language and travel website preferences. After that, landmarks with the top-6 highest satisfaction are provided to the user for a one-day visit plan in Tokyo. Experimental results demonstrate that the proposed algorithm can recommend landmarks that fit the user's preferences and our algorithm also successfully predicts the user's landmark satisfaction with a low error rate less than 7%, which is superior to other previous studies.

    DOI

  • Road-illuminance level inference across road networks based on Bayesian analysis

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    2018 IEEE International Conference on Consumer Electronics, ICCE 2018   2018-January   1 - 6  2018.03  [Refereed]

     View Summary

    © 2018 IEEE. This paper proposes a road-illuminance level inference method based on the naive Bayesian analysis. We investigate quantities and types of road lights and landmarks with a large set of roads in real environments and reorganize them into two safety classes, safe or unsafe, with seven road attributes. Then we carry out data learning using three types of datasets according to different groups of the road attributes. Experimental results demonstrate that the proposed method successfully classifies a set of roads with seven attributes into safe ones and unsafe ones with the accuracy of more than 85%, which is superior to other machine-learning based methods and a manual-based method.

    DOI

  • Personalized one-day travel with multi-nearby-landmark recommendation

    Siya Bao, Masao Yanagisawa, Nozomu Togawa

    IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin   2017-September   239 - 242  2017.12  [Refereed]

     View Summary

    © 2017 IEEE. Travel route recommendation can strongly influence users' satisfaction and the success of touristic businesses. This paper proposes a personalized travel recommendation algorithm with time planning. We use landmark categorization and region clustering to obtain effective elements. Then we build a travel map to generate all possible travel routes. Our proposed algorithm has higher precision in landmark recommendation and time planning than thoes in previous algorithms.

    DOI

  • A safe and comprehensive route finding algorithm for pedestrians based on lighting and landmark conditions

    Siya Bao, Tomoyuki Nitta, Masao Yanagisawa, Nozomu Togawa

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences   E100A ( 11 ) 2439 - 2450  2017.11  [Refereed]

     View Summary

    Copyright © 2017 The Institute of Electronics, Information and Communication Engineers. In this paper, we propose a safe and comprehensive route finding algorithm for pedestrians based on lighting and landmark conditions. Safety and comprehensiveness can be predicted by the five possible indicators: (1) lighting conditions, (2) landmark visibility, (3) landmark effectiveness, (4) turning counts along a route, and (5) road widths. We first investigate impacts of these five indicators on pedestrians' perceptions on safety and comprehensiveness during route findings. After that, a route finding algorithm is proposed for pedestrians. In the algorithm, we design the score based on the indicators (1), (2), (3), and (5) above and also introduce a turning count reduction strategy for the indicator (4). Thus we find out a safe and comprehensive route through them. In particular, we design daytime score and nighttime score differently and find out an appropriate route depending on the time periods. Experimental simulation results demonstrate that the proposed algorithm obtains higher scores compared to several existing algorithms. We also demonstrate that the proposed algorithm is able to find out safe and comprehensive routes for pedestrians in real environments in accordance with questionnaire results.

    DOI

  • A safe and comprehensive route finding method for pedestrian based on lighting and landmark

    Siya Bao, Tomoyuki Nitta, Kazuaki Ishikawa, Masao Yanagisawa, Nozomu Togawa

    2016 IEEE 5th Global Conference on Consumer Electronics, GCCE 2016    2016.12  [Refereed]

     View Summary

    © 2016 IEEE. This paper proposes a safe and comprehensive route finding method for pedestrians. We evaluate five factors that do relieve pedestrians' fear of darkness. Based upon the evaluation, we propose a comprehensive route finding method by taking road width and reduction on turning points into consideration. The experimental results on real outdoor environments under different lighting situations confirm that the proposed method can obtain safety and comprehensive routes for pedestrians.

    DOI

  • A landmark-based route recommendation method for pedestrian walking strategies

    Siya Bao, Tomoyuki Nitta, Daisuke Shindou, Masao Yanagisawa, Nozomu Togawa

    2015 IEEE 4th Global Conference on Consumer Electronics, GCCE 2015     672 - 673  2016.02  [Refereed]

     View Summary

    © 2015 IEEE. This paper proposes a landmark-based route recommendation method for enjoyable walking atmosphere strategies by accumulating and analyzing geographical information. We utilize landmark categorization and region clustering to obtain effective elements. Experimental results demonstrate that our proposed method improves walking environment quality and confirm that it is applicable in both urban and rural areas.

    DOI

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Industrial Property Rights

  • 組合せ最適化装置、組合せ最適化方法、およびプログラム

    巴 徳瑪, 新井 淳也, 八木 哲志, 寺本 純司, 川上 蒼馬, 武笠 陽介, 鮑 思雅, 戸川 望

    Patent

Awards

  • Best Student Opponent

    2020.01  

    Winner: IEEE ICSC 2020

Research Projects

  • Applications of Large-scale Real-world Geospatial Optimization Problems Using Ising Machines

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists

    Project Year :

    2021.04
    -
    2024.03
     

Specific Research

  • Document-level Sentiment Classification in Japanese Travel Comments of Heterogeneous Sources

    2020  

     View Summary

     A user's travel satisfaction is directly and explicitly reflected in their comments compared with the other types of travelogues such as GPS trajectory and check-in data. In this advantage of user comments, it is aiming at shedding lights on determinants of travel satisfaction to serve personalized travel route recommendation. To obtain a large dataset, 479,799 user comments are collected in Tokyo, Kyoto, and Sapporo from three travel websites including TripAdvisor, Jaran, and Ctrip. Prior works have been elaborated on data-source-specific and language-specific analysis. It is found that landmark coverages vary among different websites and users have diverse satisfaction on landmarks depending on their frequently used languages and travel websites. With those findings, a personalized travel route recommendation algorithm is proposed that (1) recommends top-6 personalized landmarks and (2) generates a realistic travel route for a one-day visit. Experimental results confirm the advantages of the proposed algorithm beyond previous studies from the viewpoints of landmark recommendation precision and travel time optimization.

 

Syllabus

Teaching Experience

  • Laboratory for Science and. Engineering 2A/2B

    Waseda University  

    2020.09
    -
    Now
     

  • Introduction to C programming

    Waseda University  

    2020.04
    -
    Now
     

 

Committee Memberships

  • 2022.04
    -
    Now

    情報処理学会  高度交通システムとスマートコミュニティ研究会