Hybrid Recommender Systems for Next Purchase Prediction Based on Optimal Combination Weights

Kurzfassung/Abstract

Recommender systems (RS) play a key role in e-commerce by preselecting presumably interesting products for customers. Hybrid RSs using a weighted average of individual RSs’ predictions have been widely adopted for improving accuracy and robustness over individual RSs. While for regression tasks, approaches to estimate optimal weighting schemes based on individual RSs’ out-of-sample errors exist, there is scant literature in classification settings. Class prediction is important for RSs in e-commerce, as here item purchases are to be predicted. We propose a method for estimating weighting schemes to combine classifying RSs based on the variance-covariance structures of the errors of individual models' probability scores. We evaluate the approach on a large real-world ecommerce data set from a European telecommunications provider, where it shows superior accuracy compared to the best individual model as well as a weighting scheme that averages the predictions using equal weights.