Similarity-ranking method based on semantic computing for a context-aware system

Motoki Yokoyama; Yasushi Kiyoki; Tetsuya Mita

doi:10.1109/KCIC.2016.7883620

Similarity-ranking method based on semantic computing for a context-aware system

Motoki Yokoyama, Yasushi Kiyoki, Tetsuya Mita

Faculty of Environment and Information Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Among the enormous variety of data in recent years, transportation data contain significant potential for understanding the information requirements and intention of passengers. In this paper, we propose a new information ranking method for passenger intention prediction and service recommendation. The method includes three main features, which include (1) predicting the intention of a used based on his/her current context, (2) selecting a subspace for service recommendation, and (3) ranking the services by the highest relevant order. By comparing the predicted results with a straightforward computation method, the experimental studies show the effectiveness and efficiency of the proposed method. The paper also describes the simplicity of our method over existing subspace selection methods.

Original language	English
Title of host publication	2016 International Conference on Knowledge Creation and Intelligent Computing, KCIC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	21-27
Number of pages	7
ISBN (Electronic)	9781509052318
DOIs	https://doi.org/10.1109/KCIC.2016.7883620
Publication status	Published - 2017 Mar 20
Event	5th International Conference on Knowledge Creation and Intelligent Computing, KCIC 2016 - Manado, Indonesia Duration: 2016 Nov 15 → 2016 Nov 17

Other

Other	5th International Conference on Knowledge Creation and Intelligent Computing, KCIC 2016
Country/Territory	Indonesia
City	Manado
Period	16/11/15 → 16/11/17

Keywords

Context Awareness
Information Integration
Information Retrieval
Semantic Associative Search

ASJC Scopus subject areas

Computer Science Applications
Artificial Intelligence

Access to Document

10.1109/KCIC.2016.7883620

Cite this

Similarity-ranking method based on semantic computing for a context-aware system. / Yokoyama, Motoki; Kiyoki, Yasushi; Mita, Tetsuya.
2016 International Conference on Knowledge Creation and Intelligent Computing, KCIC 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 21-27 7883620.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yokoyama, M, Kiyoki, Y & Mita, T 2017, Similarity-ranking method based on semantic computing for a context-aware system. in 2016 International Conference on Knowledge Creation and Intelligent Computing, KCIC 2016., 7883620, Institute of Electrical and Electronics Engineers Inc., pp. 21-27, 5th International Conference on Knowledge Creation and Intelligent Computing, KCIC 2016, Manado, Indonesia, 16/11/15. https://doi.org/10.1109/KCIC.2016.7883620

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abstract = "Among the enormous variety of data in recent years, transportation data contain significant potential for understanding the information requirements and intention of passengers. In this paper, we propose a new information ranking method for passenger intention prediction and service recommendation. The method includes three main features, which include (1) predicting the intention of a used based on his/her current context, (2) selecting a subspace for service recommendation, and (3) ranking the services by the highest relevant order. By comparing the predicted results with a straightforward computation method, the experimental studies show the effectiveness and efficiency of the proposed method. The paper also describes the simplicity of our method over existing subspace selection methods.",

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AU - Kiyoki, Yasushi

AU - Mita, Tetsuya

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N2 - Among the enormous variety of data in recent years, transportation data contain significant potential for understanding the information requirements and intention of passengers. In this paper, we propose a new information ranking method for passenger intention prediction and service recommendation. The method includes three main features, which include (1) predicting the intention of a used based on his/her current context, (2) selecting a subspace for service recommendation, and (3) ranking the services by the highest relevant order. By comparing the predicted results with a straightforward computation method, the experimental studies show the effectiveness and efficiency of the proposed method. The paper also describes the simplicity of our method over existing subspace selection methods.

AB - Among the enormous variety of data in recent years, transportation data contain significant potential for understanding the information requirements and intention of passengers. In this paper, we propose a new information ranking method for passenger intention prediction and service recommendation. The method includes three main features, which include (1) predicting the intention of a used based on his/her current context, (2) selecting a subspace for service recommendation, and (3) ranking the services by the highest relevant order. By comparing the predicted results with a straightforward computation method, the experimental studies show the effectiveness and efficiency of the proposed method. The paper also describes the simplicity of our method over existing subspace selection methods.

KW - Context Awareness

KW - Information Integration

KW - Information Retrieval

KW - Semantic Associative Search

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BT - 2016 International Conference on Knowledge Creation and Intelligent Computing, KCIC 2016

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ER -

Similarity-ranking method based on semantic computing for a context-aware system

Abstract

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Keywords

ASJC Scopus subject areas

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