机器学习Machine Learning – Andrew NG courses学习笔记
Recommender Systems 推荐系统
{an important application of machine learning}
Problem Formulation 问题规划
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1. to allow 0 to 5 stars as well,because that just makes some of the math come out just nicer.2. for this example, I have loosely 3 romantic or romantic comedy movies and 2 action movies.3. to look through the data and look at all the movie ratings that are missing and to try topredictwhat these values of the question marks should be.
Content Based Recommendations基于内容的推荐
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1. add an extra feature interceptor feature X0, which is equal to 1
2. set n to be the number of features, not counting this X zero intercept term so n is equal to two because we have two features x1 and x2
3. to make predictions, we could treat predicting the ratings of each user as aseparate linear regression problem. So specifically lets say that for each user j we are going to learn a parameter vector theta J which would be in r n+1, where n is the number of features,and we’re going to predict user J as rating movie I, with just the inner product between the parameters vector theta and the features "XI".
4. let’s say that you know we have somehow already gotten a parameter vector theta 1 for Alice.{线性规划求出：对于Alice评过的每部电影就是一个example,其中example0中x = [0.9 0], y = 5,用梯度下降求出theta}Optimization algorithm:estimate of parameter vector theta j
Note:
1. to simplify the subsequent math,get rid of this term MJ.that’s just a constant.2. because our regularization term here regularizes only the values of theta JK for K not equal to zero.we don’t regularize theta 0.
3. can also plug them into a more advanced optimization algorithm like cluster gradient or L-BFGS and use that to try to minimize the cost function J as well.
4. content based recommendations,because we assume that we have features for the different movies.that capture what is the content of these movies. How romantic/action is this movie?And we are really using features of the content of the movies to make our predictions.
5. Suppose there is only one user and he has rated every movie in the training set. This implies that and for every . In this case, the cost function is equivalent to the one used for regularized linear regression.
Collaborative Filtering协同过滤
{CF has an interesting property:feature learning can start to learn for itself what features to use}
Note:we do not know the values of these features of movies.But assume we’ve gone to each of our users, and each of our users has told us how much they like the romantic movies and how much they like action packed movies.each user J just tells us what is the value of theta J for them.
Optimization algorithm
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旅行，重复一个承诺和梦想，听他第二十八次提起童年往事，
