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Context-Similarity Collaborative Filtering Recommendation
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DOI：10.1109/ACCESS.2020.2973755.png)
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摘要
En 中文
This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the users ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models.
Keyword:
Context modeling
Collaboration
Data models
Motion pictures
Predictive models
Recommender systems
CIBCF models
contextual attributes
context-similarity matrix
CUBCF models
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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