An Ensemble Algorithm Used in Video Recommendation System

At present, there are many recommendation algorithms, but no one is superior to another under any background or any data. In order to improve the performance of recommendation algorithm as much as possible, we propose an ensemble algorithm by combining two algorithms, one is a recommendation algorithm based on item clustering and matrix factorization, the other is collaborative filtering algorithm based on data smoothing. The former performs well in the domain of digital television, but it has not solved the data sparsity problem. The latter can decrease data sparsity, but it is poor in dealing with cold-start problem. 2We combine the two algorithms together by computing their weighted sums according to the predicting scores. Finally, compared with the two algorithms, the new ensemble algorithm performs better in video recommendation system.

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