Deep ensembled multi-criteria recommendation system for enhancing and personalizing the user experience on e-commerce platforms

The commercially applicable Recommendation system (RS) exploits multi-criteria rating-based user-item interaction to learn and personalize user preferences using the Multi-criteria recommendation system (MCRS). The existing MCRS techniques have exploited similarity or aggregation function-based modeling to improve prediction accuracy. However, these MCRS methods do not investigate item aspects-based latent user preferences and criteria-based user-item implicit relationships. Also, the prediction reliability is uncertain due to highly sparse user-item interactions and ignoring auxiliary information support. Hence, this study proposes an ensembled approach that jointly develops the Similarity and aggregation function-based MCRS model (SimAgg-MCRS) and aggregates their user-item predicted preferences into a cumulative preference matrix to generate the final recommendation. First, the proposed model develops the deep neural network (DNN)-based model to aggregate the criteria-based similarity and predicts the overall rating using the aggregated similarity by merging user and item-based predictions. Second, the preference relation-based aggregation function approach develops deep autoencoder-based modeling to exploit the latent relationship among criteria to obtain users’ overall preference over an item by aggregating criteria-wise preference. Finally, the third phase develops the DNN-based ensemble model to integrate the preference matrix of similarity and aggregation function approach to obtain the overall aggregated matrix for the recommendation. The proposed SimAgg-MCRS integrates user and item side information to learn user preferences better. Experimental and prediction accuracy-based comparative evaluation results across Yahoo! Movies and Trip Advisor multi-criteria datasets validate the proposed models’ performance over the baseline MCRS methods.