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ISVD engine

The algorithm used by this engine uses collaborative filtering based on latent factor models. It considers the past behavior of users to profile both users and items, establishing connections between them.

ISVD engine diagram

This engine is meant to work on environments where only implicit feedback is available. For instance, websites where the clicks by the users are the only aqcuirable data to analyze are examples of suitable candidates for the ISDV Engine.

Usage

Data

The first step to make recommendations is to load items, users and interactions data into the system. It can be imported individually or in batch.

Accepted interaction types:

unarybinarycategoricalcontinuous

Training

In order to make recommendations, the engine needs to be trained. Then the model is ready to make recommendations.

You can find more details about the training in Training.

Recommendations

After the model has been trained, recommendations can be made. This engine can make recommendations for the following user-item combinations:

UsersItems
Users
Items

Diversity

Model tags are required to apply diversification. The tags extraction is done by training the model with the Content Based engine.

Output

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.