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协同过滤中Top-N推荐的概率方法分析
An Analysis of Probabilistic Methods for Top-N Recommendation in Collaborative Filtering |
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| 课程网址: |
http://videolectures.net/ecmlpkdd2011_barbieri_filtering/
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| 主讲教师: |
Nicola Barbieri
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| 开课单位: |
雅虎公司
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| 开课时间: |
2011-11-30
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| 课程语种: |
英语
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| 中文简介: |
在这项工作中，我们根据不同的验证视角对推荐的概率方法进行分析，该方法侧重于准确度指标，如召回和推荐列表的精确度。传统上，对推荐的在这项工作中，我们从不同的验证角度对推荐的概率方法进行了分析，验证的重点是准确性指标，如推荐列表的查全率和精确度。传统上，最先进的推荐方法从“缺失价值预测”的角度来考虑推荐过程。这种方法简化了基于最小化标准错误度量(如RMSE)的模型验证阶段。然而，最近的研究指出了这种方法的几个局限性，表明较低的RMSE并不一定意味着具体建议方面的改进。我们证明，在生成推荐列表的灵活性方面，潜在的概率框架比传统方法提供了一些优势，从而提高了推荐的准确性。现有技术方法从“缺失值预测”的角度考虑推荐过程。此方法简化了模型验证阶段，该阶段基于标准错误度量（例如RMSE）的最小化。然而，最近的研究指出了这种方法的一些局限性，表明较低的RMSE并不一定意味着在具体建议方面有所改进。我们证明了基础概率框架在推荐列表生成的灵活性方面提供了优于传统方法的若干优势，从而提高了推荐的准确性。
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| 课程简介: |
In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art approaches to recommendations consider the recommendation process from a "missing value prediction" perspective. This approach simplifies the model validation phase that is based on the minimization of standard error metrics such as RMSE. However, recent studies have pointed several limitations of this approach, showing that a lower RMSE does not necessarily imply improvements in terms of specific recommendations. We demonstrate that the underlying probabilistic framework offers several advantages over traditional methods, in terms of flexibility in the generation of the recommendation list and consequently in the accuracy of recommendation.
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| 关 键 词: |
概率分析; 准确度指标; 推荐方法; 推荐列表; RMSE方法
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| 课程来源: |
视频讲座网公开课
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| 最后编审: |
2020-06-29：wuyq
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| 阅读次数: |
100
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