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A Hybrid Model Based on LFM and BiGRU Toward Research Paper Recommendation
- Source :
- IEEE Access, Vol 8, Pp 188628-188640 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.
- Subjects :
- Word embedding
General Computer Science
Computer science
Feature vector
Feature extraction
02 engineering and technology
Semantics
computer.software_genre
LFM
Matrix decomposition
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Recommender systems
General Materials Science
BiGRU
user attention
Artificial neural network
business.industry
Deep learning
General Engineering
deep learning
TK1-9971
020201 artificial intelligence & image processing
Artificial intelligence
Data mining
Electrical engineering. Electronics. Nuclear engineering
business
computer
Word (computer architecture)
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....803e75e3bef52f5cab72d46ab5528de6