# Running Different Models¶

Here, we present how to run different models in RecBole.

## Proper Parameters Configuration¶

Since different categories of models have different requirements for data processing and evaluation setting, we need to configure these settings appropriately.

The following will introduce the parameter configuration of these four categories of models: namely general recommendation, context-aware recommendation, sequential recommendation and knowledge-based recommendation.

### 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
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 needs to group data by user and perform negative sampling.

group_by_user: True
training_neg_sample_num: 1


### Context-aware Recommendation¶

load the feature columns

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

We also need to configure LABEL_FIELD, which represents the label column in the CTR prediction. For the Context-aware recommendation models, the setting of LABEL_FIELD is divided into two cases:

1) There is a label field in atomic file, and the value is in 0/1, we only need to set as follows:

LABEL_FIELD: label


2) There is no label field in atomic file, we need to generate label field based on some information.

LABEL_FIELD: label
threshold:
rating: 3


rating is a column in atomic file and is loaded (by load_col). In this way, the label of the interaction with rating >= 3 is set to 1, the reset are set to 0.

training and evaluation settings

Context-aware recommendation models usually does not need to group data by user and perform negative sampling.

group_by_user: False
training_neg_sample_num: 0


Since there is no need to rank the results, eval_setting only needs to set the first part, for example:

eval_setting: RO_RS


The evaluation metrics are generally set to AUC and LogLoss.

metrics: ['AUC', 'LogLoss']


### 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
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