Personalized literature recommendation based on heterogeneous entity academic network

Qin, Hongwu; Han, Xianzhe; Ma, Xiuqin; Yan, Wenying

doi:10.1016/j.jksuci.2023.101649

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A heterogeneous graph neural network was proposed by Zhang et al [30], which enriches the created graph characteristics with information from various node types and connectivity interactions. The model has shown good application in link prediction [31], node classification [32], and personalized recommendation [33]. A heterogeneous graph convolutional network sentiment classification model was presented by Zhang et al [18].…”

Section: Graph Convolutional Networkmentioning

confidence: 99%

Knowledge-Guided Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Analysis

Song,

Ling,

et al. 2024

Electronics

View full text Add to dashboard Cite

The purpose of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity of aspects in a given sentence. Most historical works on sentiment analysis used complex and inefficient methods to integrate external knowledge. Furthermore, they fell short of completely utilizing BERT’s potential because when trying to generate word embeddings, they merely averaged the BERT subword vectors. To overcome these limitations, we propose a knowledge-guided heterogeneous graph convolutional network for aspect-based sentiment analysis (KHGCN). Specifically, we consider merging subword vectors utilizing a dynamic weight mechanism in the BERT embedding layer. Additionally, heterogeneous graphs are constructed to fuse different feature associations between words, and graph convolutional networks are utilized to identify context-specific syntactic features. Furthermore, by embedding a knowledge graph, the model can learn additional features from sources other than the corpus. Based on this knowledge, it is consequently possible to obtain more knowledge representation for a particular aspect by utilizing the attention mechanism. Last but not least, semantic features, syntactic features, and knowledge are dynamically combined using feature fusion. Experiments on three public datasets demonstrate that our model achieves accuracy rates of 80.87%, 85.42%, and 91.07%, which is an improvement of more than 2% compared to other benchmark models based on HGCNs and BERT.

show abstract

Section: Graph Convolutional Networkmentioning

confidence: 99%

Knowledge-Guided Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Analysis

Song,

Ling,

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Content-based filtering (CBF) is built based on the assumption that users like products/items with features that are available in the past and future [3]. The features entered include information from the user, such as gender, age, etc.…”

Section: Introductionmentioning

confidence: 99%

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Saifudin,

Widiyaningtyas

2024

IEEE Access

View full text Add to dashboard Cite

Recommender systems are needed with the presence of the internet and social media. The benefits that are felt in the recommender system can make it easier for users to find suitable products and recommend other products, specifically with lots of information. Recommender systems continue to develop over time. This has led many researchers to continue to find the latest approach and evaluation techniques by comparing the performance of previously existing recommender systems. The main approaches that are often used in recommender systems are Content-Based Filtering (CBF), Collaborative Filtering (CF), and Hybrid Filtering (HBF). This time, we focus on conducting a Systematic Literature Review (SLR) of several research articles and analyzing methods for algorithms developed in building recommender systems. The SLR method consists of three stages: planning, implementation, and reporting. The research used as a comparison is between 2019-2023 using various existing data sets. There were 72 primary studies where the Collaborative Filtering method was used in 46 studies, Content-Based Filtering was used in 11 studies, and the Hybrid Filtering method was used in 15 studies. The results of this SLR process show the advantages and disadvantages of each method and type of evaluation developed in building a recommender system. Apart from that, several challenges arise with various existing problems. However, the model-based collaborative filtering method is one method that can minimize the problems of cold start, data sparsity, and scalability.

show abstract

Personalized literature recommendation based on heterogeneous entity academic network

Cited by 2 publications

References 25 publications

Knowledge-Guided Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Analysis

Knowledge-Guided Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Analysis

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Contact Info

Product

Resources

About