| 研究課題/領域番号 |
19F19704
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| 研究機関 | 東京工科大学 |
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研究代表者 |
大山 恭弘 東京工科大学, 工学部, 教授 (00233289)
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| 研究分担者 |
WANG FENG 東京工科大学, 工学部, 外国人特別研究員
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| 研究期間 (年度) |
2019-04-25 – 2021-03-31
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| キーワード | Influence Maximization / Recommendation Algorithm / Social Big Data / User Influence Eval. / Network Analysis |
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| 研究実績の概要 |
We devised a preprocess method for user influence information in social big data. First, we presented methods to identify the relevant influence information including user social relationships, hobbies, influence relationships, etc. Then, we designed flexible and efficient multi-dimensional influence information collection models and methods. Finally, we proposed an implicit representation learning approach for user influence features to improve the performance of influence measurement.
We also devised an influence deep learning (IDL) model to learn users’ latent feature representation for predicting influence spread. The IDL model is fully data-driven, and it uses sampling subnetworks as inputs to deep neural networks for learning users’ latent vector representation.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The study is progressed as schedule. We prepared the study very smoothly. So, It made it possible for me to start and focus on my study. As a result, I submitted a paper to a very famous international conference and successfully presented my paper.
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| 今後の研究の推進方策 |
Influence maximization algorithm (2020/04~2020/10)
(1) To establish a preprocess method for user influence information in online social networks. (2) To propose a user influence evaluation approach based on the social big data. Then, we plan to devise a novel influence maximization algorithm to find a balance point between effectiveness and efficiency.
Recommendation algorithm based on the top-k influential nodes (2020/10~2021/03)
(1) To propose a new method of analyzing the important role of influential users in the process of education resources recommendation. (2) To devise a group recommendation algorithm of education resources based on the top-k influential nodes. (3) To attempt to develop algorithms for the seed set detection and resource recommendation.
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