On Information Fusion in Recommender Systems Based on Dempster-Shafer Theory
Van-Doan Nguyen, Van‐Nam Huynh · 2016
In this paper, we address the problem of combining information in recommender systems (RSs) based on Dempster-Shafer theory (DST). We first discuss the characteristics of this problem, and then analyze six popular combination methods in the context of RSs. Based on the analysis, we propose two new mixed combination methods which can be considered as useful tools for fusing information in the systems. To evaluate the proposed methods, we integrate them into a typical RS based on DST, and then measure recommendation performances on MovieLens data set. The experimental results show that, comparing to the baselines, the new methods outperform with regards to DS-MAE and DS-Recall, and can be comparable in terms of DS-Precision and DS-F1.