Training & Evaluation Introduction

Training introduction

Multiple training strategies are supported by RecBole. For traditional CPU-based collaborative filter models, non-gradient training is naturally applied. For main-stream neural-based models, automatic gradient descent is well equipped and set as default training strategy. Also two-stage training strategy is prepared for pretraining-based models. In addition, users who need an unusual training strategy can customize the Trainer and please refer to Customize Trainers for more details.

Apart from flexible training strategies, an automatic hyper-parameter searching is also supported. The implement of searching is fully based on hyperopt. Users can set the range of hyper-parameters in a config file with format of hyperopt and the optimal hyper-parameter and result will be output. You can read Parameter Tuning for more information about hyper-parameter-tuning in RecBole.

To control the training method, we design a series of training parameters in config, and you can read the Training Settings for more information.

Evaluation introduction

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 method

The evaluation method 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

Random Ordering

TO

Temporal Ordering

LS

Leave-one-out Splitting

RS

Ratio-based Splitting

full

full ranking with all item candidates

uniN

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

popN

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

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

  • eval_args (dict): The overall evaluation settings. It contains all the setting of evaluation including split, group_by, order and mode.

    • split (dict): Control the splitting of dataset and the split ratio. The key is splitting method and value is the list of split ratio. The range of key is [RO,TO]. Defaults to {'RO':[0.8, 0.1, 0.1]}

    • group_by (str): Whether to split dataset with the group of user. Range in [None, user] and defaults to user.

    • order (str): Control the ordering of data and affect the splitting of data. Range in ['RS', 'LS'] and defaults to RS.

    • mode (str): Control different candidates of ranking. Range in [labeled, full, unixxx, popxxx] and defaults to full.

  • repeatable (bool): Whether to evaluate the result with a repeatable recommendation scene. Note that it is disabled for sequential models as the recommendation is already repeatable. For other models, defaults to False.

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, MRR and GAUC, measuring the ranking performance of the generated recommendation lists by an algorithm. Besides, several ranking-based non-accuracy metrics are supported to evaluate in different views, such as ItemCoverage, AveragePopularity, GiniIndex, ShannonEntropy and TailPercentage. More details about metrics can refer to recbole.evaluator.metrics.

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 the following table:

    Type

    Metrics

    Ranking-based

    Recall, MRR, NDCG, Hit, MAP, Precision, GAUC, ItemCoverage, AveragePopularity, GiniIndex, ShannonEntropy, TailPercentage

    value-based

    AUC, MAE, RMSE, LogLoss

    Note that value-based metrics and ranking-based metrics can not be used together.

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

For more details about evaluation settings, please read Evaluation Settings