Dr. Shun Nishide Profile

Dr. Shun Nishide

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Proceedings Article | 12 October 2020 Paper

Collaborative filtering recommendation algorithm based on bisecting K-means clustering

Jia Liu, Xin Kang, Shun Nishide, Fuji Ren

Proceedings Volume 11574, 115740Y (2020) https://doi.org/10.1117/12.2580026

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The traditional collaborative filtering recommendation algorithm has the problem of data sparsity and expansibility. Aiming at this problem, and improved bisecting k-means collaborative filtering algorithm proposed.The algorithm first removes unrated items in the rating data matrix based on the Weighted Slope One algorithm preprocessing to reduce its sparsity. Then the preprocessed rating data is clustered based on the bisecting K-means algorithm, which reduces the nearest neighbor search space of the target user by assembling similar objects, thereby improving the algorithm’s expansibility. Finally, use the recommendation algorithm to generate the final result.Experimental results show that the improved bisecting k-means algorithm improves the recommendation effect.

Proceedings Article | 12 October 2020 Paper

A fusion model for multi-label emotion classification based on BERT and topic clustering

Fei Ding, Xin Kang, Shun Nishide, Zhijin Guan, Fuji Ren

Proceedings Volume 11574, 115740D (2020) https://doi.org/10.1117/12.2579255

KEYWORDS: Classification systems, Machine learning, Data modeling, Neural networks, Mining, Feature extraction

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As one of the most critical tasks of natural language processing (NLP), emotion classification has a wide range of applications in many fields. However, restricted by corpus, semantic ambiguity, and other constraints, researchers in emotion classification face many difficulties, and the accuracy of multi-label emotion classification is not ideal. In this paper, to improve the accuracy of multi-label emotion classification, especially when semantic ambiguity occurs, we proposed a fusion model for text based on self-attention and topic clustering. We use the Pre-trained BERT to extract the hidden emotional representations of the sentence, and use the improved LDA topic model to cluster the topics of different levels of text. Then we fuse the hidden representations of the sentence and use a classification neural network to calculate the multi-label emotional intensity of the sentence. After testing on the Chinese emotion corpus Ren_CECPs corpus, extensive experimental results demonstrate that our model outperforms several strong baselines and related works. The F1-score of our model reaches 0.484, which is 0.064 higher than the best results in similar studies.

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