如何评估基于内容的推荐系统
我正在构建一个基于内容的电影推荐系统。很简单，只要让用户输入电影名称，系统就会找到具有最相似特征的电影。
计算相似度并将分数按降序排序后，找到相似度分数最高的 5 个对应的电影并返回给用户。
到目前为止，当我想评估系统的准确性时，一切都运行良好。我在 Google 上找到的一些公式只是根据评分值评估准确性（比较预测评分和实际评分，如 RMSE）。我没有将相似度得分更改为评级（从 1 到 5 的等级），因此我无法应用任何公式。
您能建议任何方法将相似性得分转换为预测评分，以便我可以应用 RMSE 吗？或者有什么解决这个问题的想法吗？
I'm building a content-based movie recommender system. It's simple, just let a user enter a movie title and the system will find a movie which has the most similar features.
After calculating similarity and sorting the scores in descending order, I find the corresponding movies of 5 highest similarity scores and return to users.
Everything works well till now when I want to evaluate the accuracy of the system. Some formulas that I found on Google just evaluate the accuracy based on rating values (comparing predicted rating and actual rating like RMSE). I did not change similarity score into rating (scale from 1 to 5) so I couldn't apply any formula.
Can you suggest any way to convert similarity score into predicted rating so that I can apply RMSE then? Or is there any idea of solution to this problem ?
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你有任何基本事实吗？例如，您是否有关于用户过去喜欢/看过/购买的电影的信息？它不一定是评级，但为了评估推荐，您需要了解有关用户偏好的一些信息。
如果这样做，那么除了 RMSE 之外还有其他方法来衡量准确性。当我们预测评级时使用 RMSE（正如您所说的是实际评级和预测之间的误差），但在您的情况下，您正在生成前 N 个推荐。在这种情况下，您可以使用精确度和召回率来评估您的建议。它们在信息检索应用程序中非常常用（请参阅维基百科），并且在推荐系统中也很常见。您还可以计算 F1 指标，它是精度和召回率的调和平均值。您会发现它们是非常简单的公式并且很容易实现。
Guy Shani 的《评估推荐系统》是一篇关于如何评估推荐系统的非常好的论文，它将让您对这一切有一个很好的了解。您可以在此处找到该论文。
Do you have any ground truth? For instance, do you have information about the movies that a user has liked/seen/bought in the past? It doesn't have to be a rating but in order to evaluate the recommendation you need to know some information about the user's preferences.
If you do, then there are other ways to measure the accuracy besides RMSE. RMSE is used when we predict ratings (as you said is the error between the real rating and the prediction) but in your case you are generating top N recommendations. In that case you can use precision and recall to evaluate your recommendations. They are very used in Information Retrieval applications (see Wikipedia) and they are also very common in Recommender Systems. You can also compute F1 metric which is an harmonic mean of precision and recall. You'll see they are very simple formulas and easy enough to implement.
"Evaluating Recommendar Systems" by Guy Shani is a very good paper on how to evaluate recommender systems and will give you a good insight into all this. You can find the paper here.