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"item_title" : "Machine Learning Paradigms",
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"item_description" : "Introduction.- Review of Previous Work Related to Recommender Systems.- The Learning Problem.-Content Description of Multimedia Data.- Similarity Measures for Recommendations based on Objective Feature Subset Selection.- Cascade Recommendation Methods.- Evaluation of Cascade Recommendation Methods.- Conclusions and Future Work.",
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Machine Learning Paradigms : Applications in Recommender Systems
Overview
Introduction.- Review of Previous Work Related to Recommender Systems.- The Learning Problem.-Content Description of Multimedia Data.- Similarity Measures for Recommendations based on Objective Feature Subset Selection.- Cascade Recommendation Methods.- Evaluation of Cascade Recommendation Methods.- Conclusions and Future Work.
This item is Non-Returnable
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Details
- ISBN-13: 9783319384962
- ISBN-10: 3319384961
- Publisher: Springer
- Publish Date: October 2016
- Dimensions: 9.21 x 6.14 x 0.31 inches
- Shipping Weight: 0.47 pounds
- Page Count: 125
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