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In recommendation algorithms, commonly used evaluation indicators include Accuracy, Precision, Recall, and AUC. These indicators are used to evaluate the performance and effect of recommendation algorithms.
1. Accuracy: Accuracy refers to the proportion of correct predictions among all predicted results. In a recommendation system, accuracy indicates how many of the predicted recommended results are actually of interest to the user. The accuracy ranges from 0 to 1, and the higher the value, the more accurate the recommendation result.
2. Precision: Precision refers to the proportion of truly positive samples among all predicted positive samples. In a recommendation system, precision indicates how many of the items recommended to the user are actually of interest to the user. The precision ranges from 0 to 1, and the higher the value, the higher the proportion of items of interest to the user included in the recommendation results.
3. Recall: Recall refers to the proportion of successfully predicted positive samples among all true positive samples. In a recommendation system, recall indicates how many items that the user is actually interested in are successfully recommended to the user. The recall rate ranges from 0 to 1. The higher the value, the higher the proportion of items of interest to the user in the recommended results.
4. AUC (Area Under the Curve): AUC is an indicator used to evaluate the ranking performance of the recommendation system. It indicates the ranking ability of the recommendation algorithm for positive and negative samples, that is, the probability that the recommendation results can be correctly sorted among all positive and negative sample pairs. The AUC value ranges from 0.5 to 1. The closer the value is to 1, the better the sorting ability.
It should be noted that the interpretation and use of these evaluation indicators may vary depending on the specific recommendation algorithm and application scenario. In specific applications, appropriate evaluation indicators can be selected according to business needs and algorithm characteristics to evaluate the performance of the recommendation algorithm.

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