An adaptive framework for personalized recommendation algorithms
Jianchang Tang, Xinhuai Tang · 2014
Different personalized recommendation algorithms are suitable for different scenarios. In this paper, we use artificial neural networks to implement an adaptive framework. When we add different recommendation algorithms into it and train it with the data from a given scenario, it can calculate the weight of each algorithm, choose suitable algorithms and give a more accurate prediction rating.