Recommendation Systems

Regression Recommendation Systems

• Creating Probability-Based Recommendation Model

  • Minimal implementation takes a minimum of 2 hours using DataPredict™.

• Creating Similarity-Based Recommendation Model

  • Memory-based model. May eat up storage space.

  • Minimal implementation takes a minimum of 2 hours using DataPredict™.

• Creating Reward-Maximization-Based Regression Recommendation Model

  • Limited to one recommendation at a time.

  • Have higher monetization potential due to its ability to exploit and explore than the other two models, but tend to be risky to use.

  • Minimal implementation takes a minimum of 2 hours using DataPredict™, especially if multiple recommendations are made.

• Creating Reward-Maximization-Based Regression Recommendation Model (Aqwam’s Style)

  • Similar to the previous model.

  • Faster convergence due to taking advantage of the probabilistic nature of input values.

  • Minimal implementation takes a minimum of 2 hours using DataPredict™, especially if multiple recommendations are made.

Binary Recommendation Systems

• Creating Classification-Based Recommendation Model

  • Minimal implementation takes a minimum of 2 hours using DataPredict™.

• Creating Reward-Maximization-Based Binary Recommendation Model

  • Limited to one recommendation at a time.

  • Have higher monetization potential due to its ability to exploit and explore than the classification-based model, but tend to be risky to use.

  • Minimal implementation takes a minimum of 2 hours using DataPredict™, especially if multiple recommendations are made.