Research on semantic representation and citation recommendation of scientific papers with multiple semantics fusion

Lu, Yonghe; Yuan, Meilu; Liu, Jiaxin; Chen, Minghong

doi:10.1007/s11192-022-04566-5

Cited by 12 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For text sentiment analysis, equal-length convolution [39] can be employed to gather contextual information for each word. This technique compresses the contextual information of each word by considering its neighboring words, thereby enriching the semantic understanding of individual words.…”

Section: Edc Modelmentioning

confidence: 99%

Sentiment Analysis of Online New Energy Vehicle Reviews

Wang

You

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Massive online reviews of new energy vehicles in China are deemed crucial by companies, as they offer valuable insights into user demands and perceptions. An effective analysis enables companies to swiftly adapt and enhance their products while upholding a positive public image. Nonetheless, the sentiment analysis of online car reviews can pose challenges due to factors such as incomplete context, abrupt information bursts, and specialized vocabulary. In this paper, an enhanced hybrid model is introduced, combining Enhanced Representation through kNowledge IntEgration (ERNIE) and a deep (Convolutional Neural Network) CNN, to tackle these challenges. The model utilizes fine-tuned ERNIE for feature extraction from preprocessed review datasets, generating word vectors that encompass comprehensive semantic information. The deep CNN component captures local features from the text, thereby capturing semantic nuances at multiple levels. To address sudden shifts in public sentiment, a channel attention mechanism is employed to amplify the significance of crucial information within the reviews, facilitating comment relationship classification and sentiment prediction. The experimental results demonstrate the efficacy of the proposed model, achieving an impressive accuracy rate of 97.39% on the test set and significantly outperforming other models.

show abstract

Section: Edc Modelmentioning

confidence: 99%

Sentiment Analysis of Online New Energy Vehicle Reviews

Wang

You

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Collaborative filtering-based approaches assess a user's reading records and predict the user's preferences for unread papers using methods such as nearest neighbor computation, matrix decomposition [5], and deep learning [6]. Graph-based approaches, which often use homomorphic graphs, such as citation networks [7][8][9], or heteromorphic graphs [10,11], like those constructed by entities such as authors-conferencespapers, generate embeddings of the entities. They then create recommendation lists via meta-path methods [12] or graph neural networks [13].…”

Section: Introductionmentioning

confidence: 99%

A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance Problem

Huang,

Liu,

Wang

2024

Electronics

View full text Add to dashboard Cite

Paper recommendation systems are important for alleviating academic information overload. Such systems provide personalized recommendations based on implicit feedback from users, supplemented by their subject information, citation networks, etc. However, such recommender systems face problems like data sparsity for positive samples and uncertainty for negative samples. In this paper, we address these two issues and improve upon them from the perspective of metric learning. The algorithm is modeled as a push–pull loss function. For the positive sample pull-out operation, we introduce a context factor, which accelerates the convergence of the objective function through the multiplication rule to alleviate the data sparsity problem. For the negative sample push operation, we adopt an unbiased global negative sample method and use an intermediate matrix caching method to greatly reduce the computational complexity. Experimental results on two real datasets show that our method outperforms other baseline methods in terms of recommendation accuracy and computational efficiency. Moreover, our metric learning method that introduces context improves by more than 5% over the element-wise alternating least squares method. We demonstrate the potential of metric learning in addressing the problem of implicit feedback recommender systems with positive and negative sample imbalances.

show abstract