This engine applies a mixing of many of the previous engines to make recommendations. The engine needs to be configured by setting a weight for every engine included in the setup. Then, it makes recommendations based on a weighted combination of the scores calculated by each of the individual engines.
The accepted interaction types depend on the engines configured in the ensemble. All the engines in the setup must support the interaction type used to make recommendations. The supported interaction types of each engine are specified in the data section of their documentation.
Before training, the ensemble configuration must be created with the desired engines and weights. Additional information on how to configure it can be found in Ensemble configuration.
In order to make recommendations, the configured engines need to be trained. Then the model is ready to make recommendations.
You can find more details about the training in Training.
After the models have been trained, recommendations can be made. The user-item combinations depend on the engines configured in the ensemble. Every engine in the setup must support the recommendation type to make.
Supported interaction types:
|Factorization Machines (FM)||✓||✓||✓||✓|
|Factorization Machines (FM)||✓||✓|
Model tags are required to apply diversification. The tags extraction is done by training the model with the Content Based engine.
Every entity included in the list of recommendations comes with a percent match score. This score, a number between 0 and 100, indicates the degree of affinity between the recommended entity and the recipient.