Review 15 [coursera] Machine learning - Stanford University - Andrew Ng

本文探讨了推荐系统的应用场景,包括基于用户行为为在线书店顾客推荐书籍、预测书籍销量、个性化新闻文章推荐系统及基于用户偏好筛选新闻文章等。通过对比协同过滤与其他机器学习算法如线性回归和逻辑回归的应用场景,明确了推荐系统的优势。

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Recommender Systems

In which of the following situations will a collaborative filtering system be the most appropriate learning algorithm (compared to linear or logistic regression)?

You manage an online bookstore and you have the book ratings from many users. For each user, you want to recommend other books she will enjoy, based on her own ratings and the ratings of other users.

You manage an online bookstore and you have the book ratings from many users. You want to learn to predict the expected sales volume (number of books sold) as a function of the average rating of a book.

You've written a piece of software that has downloaded news articles from many news websites. In your system, you also keep track of which articles you personally like vs. dislike, and the system also stores away features of these articles (e.g., word counts, name of author). Using this information, you want to build a system to try to find additional new articles that you personally will like.

You run an online news aggregator, and for every user, you know some subset of articles that the user likes and some different subset that the user dislikes. You'd want to use this to find other articles that the user likes.

 

 

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