Hybrid recommendation model based on deep learning and Stacking integration strategy

Abstract

In the traditional recommendation algorithms, due to the rapid development of deep learning and Internet technology, user-item rating data is becoming increasingly sparse. The simple inner product interaction mode adopted by the collaborative filtering method has a cold start problem and cannot learn the complex nonlinear structural features between users and items, while the content-based algorithm encounters the difficulty of effective feature extraction. In response to this problem, a hybrid model is proposed based on deep learning and Stacking integration strategy. The traditional recommendation algorithm is first fused by using the Stacking integration strategy to make up for the shortcomings of the single recommendation algorithm to achieve better recommendation performance. The fusion-based model learns the more abstract and deeper nonlinear interaction features by deep learning technology, which makes the model performance gain further. The experiment comparison on the MovieLens-1m dataset shows that the proposed hybrid recommendation model can significantly improve the accuracy of rating prediction.