问题描述
我有一个有 129539 行和 530 列的矩阵.第一列对应于 ClientID，第一行对应于产品品牌.在里面，我有一个每个 ClientID 对每个产品品牌的排名索引(如果 ClientID 从未购买过该产品，则为 0，否则一直到 10).
I have a matrix with 129539 rows and 530 columns. The first column correspond to ClientIDs and the first row to product brands. Inside I have a ranking index that each ClientID has for every product brand (0 if the ClientID never bought the product, all the way up to 10 otherwise).
我正在 R 中构建一个基于用户的协作过滤推荐系统，使用前 5000 行进行训练，它给了我一个对我来说没有意义的输出.
I am building a User Based Collaborative Filtering Recommender System in R, using the first 5000 rows for training, and it gives me an output that doesn't make sense to me.
我必须生成它的代码如下:
The code I have to generate it is the following:
# 加载到预先计算好的亲和度数据affinity.data <-read.csv("mydirectory")affinity.matrix <- as(affinity.data,"realRatingMatrix")
# 创建模型 - U(ser) B(ased) C(ollaborative) F(iltering)Rec.model
# 为用户 1507323 推荐的前 5 项recommended.items.1507323 # 显示它们as(recommended.items.1507323, "list")
我得到的输出是一个值列表，例如:
[[1]][1] "0.0061652281134402" "0.00661813368630046" "0.0119331742243437" "0.0136147038801906"[5] "0.0138312586445367"
The output I'm getting is a list of values such as:[[1]][1] "0.0061652281134402" "0.00661813368630046" "0.0119331742243437" "0.0136147038801906"[5] "0.0138312586445367"
我期待的是我尝试推荐的品牌名称，而不是数字列表.PS:我的原始矩阵的值从 0 到 10(包括小数，不仅仅是整数)
I was expecting the names of the brands that I am trying to recommend, not a list of numbers.PS: my original matrix has values from 0 to 10 (decimals included, not only integers)
非常感谢您提供的任何帮助或说明.
Thank you very much for any help or clarification you may have.
推荐答案
这里有几个问题:首先，predict() 函数将为您选择的用户返回每个项目的预测评分.如果您想推荐 Top N 列表，您必须预测该用户对每个项目的评分，然后对评分进行排序并返回前 N 个.
There are a couple of issues here: first, the predict() function will return the predicted rating for each item for the user you chose. If you want to recommend a Top N list, you'll have to predict the rating for every item for that user, then sort the ratings and return the top N.
第二，当用户和项目从未交互时，推荐系统通常使用 NULL 或 NA 或缺失数据.您已经为此使用了 0.这意味着预测将严重偏向 0(假设大多数用户不与大多数项目交互)，并且您的预测实际上是在说明用户甚至与项目交互的概率.这可能是功能或错误，具体取决于您的用例.但是，如果您的评分 1-10 代表偏好，而 0 代表使用/未使用的二进制，那么您正在混合信息，您应该将 0 替换为 NA.
Second, recommender systems normally use NULL or NA or missing data when a user and item have never interacted. You've used 0 for this. That means that the predictions are going to be heavily skewed toward 0 (given that most users don't interact with most items) and that your predictions are actually saying the probability that a user will even interact with an item. This may be a feature or a bug, depending on your use case. But if your ratings 1-10 represent preferences, and 0 represents a binary used/not used, then you're mixing information and you should replace 0 with NA.
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