A Survey on Recommendation System Algorithm based on Restricted Boltzmann Machine
Dewen Chen · IEEE Software · 2013
Aiming at solving the problems of poor performance in recommendation system when using traditional content-based or collaborative-filter based methods,a new recommendation model was proposed to deal with recommendation task in this article. This new model was a deep structure composed of several layers of restricted boltzmann machine which were learned using a unsupervised learning method called Constrastive Divergence algorithm adopting limited steps of gibbs sampling,besides, other strategies such as pre-training and fine-tune were used to make the model trained possible. At last, this article carried out several experiments among traditional matrix decomposition and the new model,the result turned out that the new model not only performed well in speed in iteration,and even performed better in sparse data compared to the traditional ones.