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Memory Reorganization: A Symmetric Memory Network for Reorganizing Neighbors and Topics to Complete Rating Prediction
- Source :
- IEEE Access, Vol 8, Pp 81876-81886 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Using pre-trained topic information to assist in training neural networks can effectively support the completion of the rating prediction task. However, existing neural-topic methods consider only the use of topic information corresponding to current users and items without neighbors, whereas existing memory-based neighborhood approaches are inappropriate for the direct modeling of neighbors with topics. To address the limitations, we argue that memory networks have the ability to organize neighbors with corresponding topics well and can provide a general solution to this problem. To confirm our hypothesis, we propose two approaches. One is an augmented memory network to couple with and enhance existing neural-topic models. The other is a symmetric memory network activated by a memory reorganization mechanism, which is a compact and generalized method for rating prediction. The experimental results demonstrate the effectiveness of the memory reorganization mechanism and show that the two proposed methods have advantages over existing state-of-the-art topic modeling approaches.
- Subjects :
- Topic model
General Computer Science
Artificial neural network
business.industry
Computer science
Mechanism (biology)
General Engineering
Machine learning
computer.software_genre
Task (project management)
neighborhood modeling
symmetric memory
memory reorganization
General Materials Science
Artificial intelligence
neural-topic collaborative filtering
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
Rating prediction
computer
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....49f868d55cd25a64949a3405a9a4ea6d