PACIS 2019 Proceedings


With the quick rise of E-Commerce, personalized recommender systems have been created, which not only stimulate the sales of products and services but also increase customers’ loyalty. Collaborative filtering has been the most common and effective technique applied to recommendation systems. In this study, we applied multi-criteria ratings in movie preference collection for building users’ profiles of different aspects profile and we used regression coefficient, which was derived from multiple regression analysis, for each criterion preference level. In order to improve each criterion prediction quality and alleviate the sparsity problem of Collaborative filtering, we firstly combine users’ preference levels and trust-values as trust-weight in different criterions and then set trustweight threshold to filter users and to find recommendation members. In the final aggregation experiments, we observed that trust-based filtering method also carried out a lower MAE in overall rating prediction and the F1 value has a better recommendation performance. Finally, the approach we proposed can improve the recommendation quality in the multi-criteria recommendation environment.