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- W4200103653 endingPage "012025" @default.
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- W4200103653 abstract "Abstract The issue of information overload has become increasingly prominent since there are various kinds of data generated daily. A good recommendation systems can better deal with such problems. However, traditional recommendation systems for a single machine are suffering from the computing bottleneck in the environment of massive data. An individual recommendation algorithm is unable to gratify desiring users. To tackle this problem, we designed and implemented three kinds of recommendation algorithms based on big data framework in this paper. Besides, we improved the traditional recommendation algorithms leveraging the prevailing big data processing technologies. Finally, we evaluated the efficiency of the algorithm through recall rate, precision rate and coverage. Experiments show that the hybrid model-based recommendation algorithms which can be applied to the bulk data environment are better than the single recommendation algorithms." @default.
- W4200103653 created "2021-12-31" @default.
- W4200103653 creator A5070634037 @default.
- W4200103653 date "2021-12-01" @default.
- W4200103653 modified "2023-09-25" @default.
- W4200103653 title "Design of Personalized Recommendation Algorithms based on Big Data Framework" @default.
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- W4200103653 doi "https://doi.org/10.1088/1742-6596/2138/1/012025" @default.
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