海量数据挖掘MMDS week4: 推荐系统之隐语义模型latent semantic analysis

http://blog.csdn.net/pipisorry/article/details/49256457

海量数据挖掘Mining Massive Datasets(MMDs) -Jure Leskovec courses学习笔记 推荐系统Recommendation System之隐语义模型latent semantic analysis

{博客内容：推荐系统构建三大方法：基于内容的推荐content-based，协同过滤collaborative filtering，隐语义模型(LFM, latent factor model)推荐。这篇博客主要讲隐语义模型，latent semantic analysis, the idea that there is a small number of hidden factors that characterize both individuals and items. An example (which surprisingly turns out NOT to be very effective as a predictor) is to categorize movies by genres (流派e.g., sci-fi, romance) and at the same time characterize what genres individual viewers like. However, there are algorithms for handling large amounts of data and finding a small number of good hidden factors to characterize both individuals and the items they like/hate.}

最近又没时间，还是下次有空写吧╮(╯_╰)╭

PS其它

[使用LFM（Latent factor model）隐语义模型进行Top-N推荐]

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from:http://blog.csdn.net/pipisorry/article/details/49256457

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