January 2021
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4 Reads
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3 Citations
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Publications (1)
Citations (1)
... In the recommendation process, it calculates the recommendation score through the linear combination of emotional features and similarity, which is used for query-based and user-based recommendation scenarios. Li et al. [122] argued that, since users only interact with items of interest in the recommendation system, that system must retain very large amounts of personalized information and item sparsity, which seriously affects the performance of the recommendation system; therefore, they proposed a CBRrecommender method to reduce data sparsity through data classification and dynamic clustering, which makes the system run faster in large-scale recommendation research that dynamically calculates user preferences. ...
- Citing Conference Paper
January 2021