# Evaluation Support¶

The function of evaluation module is to implement commonly used evaluation protocols for recommender systems. Since different models can be compared under the same evaluation modules, RecBole standardizes the evaluation of recommender systems.

## Evaluation Settings¶

The evaluation settings supported by RecBole is as following. Among them, the first four rows correspond to the dataset splitting methods, while the last two rows correspond to the ranking mechanism, namely a full ranking over all the items or a sampled-based ranking.

Notation

Explanation

RO_RS

Random Ordering + Ratio-based Splitting

TO_LS

Temporal Ordering + Leave-one-out Splitting

RO_LS

Random Ordering + Leave-one-out Splitting

TO_RS

Temporal Ordering + Ratio-based Splitting

full

full ranking with all item candidates

uniN

sample-based ranking: each positive item is paired with N sampled negative items

The parameters used to control the evaluation settings are as follows:

• eval_setting (str): The evaluation settings. Defaults to 'RO_RS,full'. The parameter has two parts. The first part control the splitting methods, range in ['RO_RS','TO_LS','RO_LS','TO_RS']. The second part(optional) control the ranking mechanism, range in ['full','uni100','uni1000'].

• group_by_user (bool): Whether the users are grouped. It must be True when eval_setting is in ['RO_LS', 'TO_LS']. Defaults to True.

• spilt_ratio (list): The split ratio between train data, valid data and test data. It only take effects when the first part of eval_setting is in ['RO_RS', 'TO_RS']. Defaults to [0.8, 0.1, 0.1].

• leave_one_num (int): It only take effects when the first part of eval_setting is in ['RO_LS', 'TO_LS']. Defaults to 2.

## Evaluation Metrics¶

RecBole supports both value-based and ranking-based evaluation metrics.

The value-based metrics (i.e., for rating prediction) include RMSE, MAE, AUC and LogLoss, measuring the prediction difference between the true and predicted values.

The ranking-based metrics (i.e., for top-k item recommendation) include the most common ranking-aware metrics, such as Recall, Precision, Hit, NDCG, MAP and MRR, measuring the ranking performance of the generated recommendation lists by an algorithm.

The parameters used to control the evaluation metrics are as follows:

• metrics (list or str): Evaluation metrics. Defaults to ['Recall', 'MRR', 'NDCG', 'Hit', 'Precision']. Range in ['Recall', 'MRR', 'NDCG', 'Hit', 'MAP', 'Precision', 'AUC', 'MAE', 'RMSE', 'LogLoss'].

• topk (list or int or None): The value of k for topk evaluation metrics. Defaults to 10.