A Personalized Explainable Learner Implicit Friend Recommendation Method

Li, Chunying; Zhou, Biao; Lin, Wei; Tang, Zhikang; Tang, Yong; Zhang, Yanchun; Cao, Jun

doi:10.1007/s41019-023-00204-z

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…LDA model tends to perform poorly in short texts, data with sparse content, or implicit opinion expressions because it ignores contextual information by disregarding order and syntax (Li et al, 2023). At this point, additional more comprehensive contextual information is needed to assist in identifying the central theme of the text.…”

Section: Topic Clustering Extension Model Based On Lda+bert+k-meansmentioning

confidence: 99%

Challenges Confronting the Sustainability of Anti-Epidemic Policies Based on the BERT-Pair-ABSA Model

Zhu¹,

Li²,

Li³

et al. 2023

OCS

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As a major public health emergency, the COVID-19 pandemic has some uncertainties. Coupling of the uncertainties and anti-epidemic policies easily leads to the spread of negative emotions. It is challenging to maintain the sustainability of anti-epidemic measures. Therefore, this paper aimed to analyze the challenges that the sustainability of anti-epidemic measures is faced with. A topic clustering extension method was proposed, which integrated Latent Dirichlet Allocation (LDA) topic information with Bidirectional Encoder Representations from Transformers (BERT) contextual information through aspect-based sentiment analysis. In addition, this paper constructed a thesaurus of aspect words from the two stages of dynamic zero-COVID and orderly relaxation of epidemic control. This paper established the BERT-pair-ABSA model for semantic expansion of auxiliary sentences and calculated sentiment polarity to gain insight into the changes in netizens' concerns, emotional states and evolution trends at different stages. The research results showed: (1) Compared with the benchmark model, the proposed sentiment analysis model had better classification accuracy and was applicable to the sentiment classification of short texts in the epidemic situation; (2) During the dynamic zero-COVID stage, netizens paid attention to grassroots epidemic management and the scope of lockdown and epidemic control, which were closely related to both specific lockdown and control management, and the implementation of regional epidemic management; and (3) in the orderly relaxation stage of epidemic control, netizens were concerned about drug guarantee, medical care guarantee, personal health protection and health protection of special population groups, and negative emotions always dominated in drug guarantee, medical care guarantee and health protection of special groups. The negative sentiment of drug guarantee, medical care guarantee and health protection of special groups always dominated. The results provided an empirical basis for the optimization and adjustment of the anti-epidemic policies.

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Section: Topic Clustering Extension Model Based On Lda+bert+k-meansmentioning

confidence: 99%

Challenges Confronting the Sustainability of Anti-Epidemic Policies Based on the BERT-Pair-ABSA Model

Zhu¹,

Li²,

Li³

et al. 2023

OCS

View full text Add to dashboard Cite

show abstract

“…A fact in knowledge graph (KG) is expressed as a triple (h, r, t), where r indicates the relation between the head entity h and tail entity t. Large-scale KGs, such as Wordnet [1], YAGO [2], Freebase [3] and Wikidata [4], have become the vital resource for many artificial intelligence tasks, like question answering [5,6], recommendation system [7,8]. However, several relations only have few observed triples.…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Relation Prediction of Knowledge Graph via Convolutional Neural Network with Self-Attention

Zhong,

Wang,

Yue

et al. 2023

Data Sci. Eng.

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Knowledge graph (KG) has become the vital resource for various applications like question answering and recommendation system. However, several relations in KG only have few observed triples, which makes it necessary to develop the method for few-shot relation prediction. In this paper, we propose the Convolutional Neural Network with Self-Attention Relation Prediction (CARP) model to predict new facts with few observed triples. First, to learn the relation property features, we build a feature encoder by using the convolutional neural network with self-attention from the few observed triples rather than background knowledge. Then, by incorporating the learned features, we give an embedding network to learn the representation of incomplete triples. Finally, we give the loss function and training algorithm of our CARP model. Experimental results on three real-world datasets show that our proposed method improves Hits@10 by 48% on average over the state-of-the-art competitors.

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Leveraging Interactive Paths for Sequential Recommendation

Zang

Wang

et al. 2023

Lecture Notes in Computer Science

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A Personalized Explainable Learner Implicit Friend Recommendation Method

Cited by 11 publications

References 26 publications

Challenges Confronting the Sustainability of Anti-Epidemic Policies Based on the BERT-Pair-ABSA Model

Challenges Confronting the Sustainability of Anti-Epidemic Policies Based on the BERT-Pair-ABSA Model

Few-Shot Relation Prediction of Knowledge Graph via Convolutional Neural Network with Self-Attention

Leveraging Interactive Paths for Sequential Recommendation

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