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

水深无声 2022-08-09 09:55 16阅读 0赞

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

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

[皮皮blog][blog]

from:[http://blog.csdn.net/pipisorry/article/details/49256457][blog.csdn.net_pipisorry_article_details_49256457]

ref:

[blog.csdn.net_pipisorry_article_details_49256457]: http://blog.csdn.net/pipisorry/article/details/49256457
[courses]: https://class.coursera.org/mmds-003
[LFM_Latent factor model_Top-N_]: http://blog.csdn.net/harryhuang1990/article/details/9924377
[blog]: http://blog.csdn.net/pipisorry