Running Different Models

In RecBole, we have 4 categories of models, namely general recommendation, context-aware recommendation, sequential recommendation and knowledge-based recommendation. Since different categories of models have different requirements for data processing and evaluation setting, we need to configure these settings appropriately.

Here, we present some examples to show how to these four categories models in RecBole.

General Recommendation

specify and load the user and item columns

General recommendation models utilize the historical interactions between users and items to make recommendations, so it needs to specify and load the user and item columns of the dataset.

USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
load_col:
    inter: [user_id, item_id]

For some dataset, the column names corresponding to user and item in atomic files may not be user_id and item_id. Just replace them with the corresponding column names.

training and evaluation settings

General recommendation models usually need to group data by user and perform negative sampling. You can set the config like this:

eval_args:
    group_by: user
train_neg_sample_args:
    distribution: uniform
    sample_num: 1
    alpha: 1.0
    dynamic: False
    candidate_num: 0

Context-aware Recommendation

load the feature columns

Generally, context-aware recommendation models utilize the features of users, items and interactions to make CTR predictions, so it needs to load the used features.

load_col:
    inter: [inter_feature1, inter_feature2]
    item: [item_feature1, item_feature2]
    user: [user_feature1, user_feature2]

inter_feature1 refers to the column name of the corresponding feature in the inter atomic file.

label setting

In general, context-aware recommendation models are mainly used in explicit feedback scenes, so your data should have explicit feedback information and you need to set label for them. For more information about label setting, please read the Label of data.

evaluation settings

If you want to apply context-aware recommendation models for CTR predictions, you can set the config like:

eval_args:
    group_by: None
    mode: labeled
metrics: ['AUC', 'LogLoss']
valid_metric: AUC

Note that RecBole also supports to evaluate the context-aware recommendation models by full-ranking like general recommendation models, but you need to make sure that your .inter file can not load any other context information column.

Sequential Recommendation

specify and load the user, item and time columns

Sequential recommendation models utilize the historical interaction sequences to predict hte next item, so it needs to specify and load the user, item and time columns of the dataset.

USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
TIME_FIELD: timestamp
load_col:
    inter: [user_id, item_id, timestamp]

For some dataset, the column names corresponding to user, item and time in atomic files may not be user_id, item_id and timestamp, just replace them with the corresponding column names.

maximum length of the sequence

The maximum length of the sequence can be modified by setting MAX_ITEM_LIST_LENGTH

MAX_ITEM_LIST_LENGTH: 50

Knowledge-based Recommendation

specify and load the kg entity columns

Knowledge-based recommendation models utilize KG information to make recommendations, so it needs to specify and load the kg information of the dataset.

USER_ID_FIELD: user_id
ITEM_ID_FIELD: item_id
HEAD_ENTITY_ID_FIELD: head_id
TAIL_ENTITY_ID_FIELD: tail_id
RELATION_ID_FIELD: relation_id
ENTITY_ID_FIELD: entity_id
load_col:
    inter: [user_id, item_id]
    kg: [head_id, relation_id, tail_id]
    link: [item_id, entity_id]