Question

对于许多算法（例如SVD），现成的内置函数为：

predictions = algo.fit(trainset).test(testset) -打印测试集的预测收视率得分（因此，对于用户已经给出收视率的电影而言）
predictions = algo.predict(uid, iid) -预测uid的iid评分得分

但是我该如何为用户打印top N推荐的电影（即使该用户尚未看到/给某些电影定级）。我已经尝试过："algo.fit(trainset).test(data)"，但是却给我错误？

我也曾尝试使用KNN来打印用户的k nearest neighbors 在惊喜包示例中，它具有u.item文件，但是如果我想使用自己的数据（一个具有uid，iid和rating的表），如何计算用户的"raw id"和一个项目？

Answer 1

此代码段已共享from Surprise Documentation FAQ，可能会有所帮助。

from collections import defaultdict

from surprise import SVD
from surprise import Dataset


def get_top_n(predictions, n=10):
    """Return the top-N recommendation for each user from a set of predictions.

    Args:
        predictions(list of Prediction objects): The list of predictions, as
            returned by the test method of an algorithm.
        n(int): The number of recommendation to output for each user. Default
            is 10.

    Returns:
    A dict where keys are user (raw) ids and values are lists of tuples:
        [(raw item id, rating estimation), ...] of size n.
    """

    # First map the predictions to each user.
    top_n = defaultdict(list)
    for uid, iid, true_r, est, _ in predictions:
        top_n[uid].append((iid, est))

    # Then sort the predictions for each user and retrieve the k highest ones.
    for uid, user_ratings in top_n.items():
        user_ratings.sort(key=lambda x: x[1], reverse=True)
        top_n[uid] = user_ratings[:n]

    return top_n


# First train an SVD algorithm on the movielens dataset.
data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()
algo = SVD()
algo.fit(trainset)

# Than predict ratings for all pairs (u, i) that are NOT in the training set.
testset = trainset.build_anti_testset()
predictions = algo.test(testset)

top_n = get_top_n(predictions, n=10)

# Print the recommended items for each user
for uid, user_ratings in top_n.items():
    print(uid, [iid for (iid, _) in user_ratings])

在推荐系统的Surprise软件包中，如何为给定用户打印推荐的电影？

1 个答案: