Recommending Items Using Collectively Trained Models

Linear models with nonlinear attribute modeling are popularly selected for massive regression and categorization tasks with sparse inputs. Memorization of attribute interactions through a broad batch of cross-product attribute conversion is compelling and explainable, while generalization needs extra feature engineering exercise. With limited effort, deep neural networks (DNN) can be generalized exceptionally to undiscovered feature sequences. However, DNN can over-generalize and recommend lesser suited items. In this paper, we propose an ensemble of various collectively trained linear models and DNNs to blend the advantage of memorization and generalization for a recommender system (RS). We evaluated the system on MovieLens 100K Dataset, a stable benchmark dataset with 100,000 ratings on 1682 movies from 943 users. Our experiment results show that ensembling of wide & deep models significantly increased recommendation accuracy and decreased mean absolute error (MAE) and root mean square error (RMSE) in comparison with collaborative filtering techniques, deep-only and wide-only models.