SEMA: Deeply Learning Semantic Meanings and Temporal Dynamics for Recommendations

SEMA: Deeply Learning Semantic Meanings and Temporal Dynamics for Recommendations

Personalization plays an essential role in recommender systems, in which the key task is to predict the personalized rating of users on new items. Recommender systems usually apply collaborative filtering techniques to make rating prediction. In recent years, some studies pay attention on learning s...

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Bibliographic Details
Main Authors: Jia-Dong Zhang, Chi-Yin Chow
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Collaborative filtering
rating prediction
semantic meanings
temporal dynamics
deep learning
Online Access:https://ieeexplore.ieee.org/document/8471113/

Internet

https://ieeexplore.ieee.org/document/8471113/

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