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.

中文翻译：

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基于深度学习和Stacking集成策略的混合推荐模型

在传统的推荐算法中，由于深度学习和Internet技术的飞速发展，用户项目评分数据越来越稀疏。协同过滤方法采用的简单的内部产品交互模式存在一个冷启动问题，无法学习用户与物品之间复杂的非线性结构特征，而基于内容的算法则面临有效特征提取的困难。针对这一问题，提出了一种基于深度学习和堆栈集成策略的混合模型。首先通过使用Stacking集成策略融合传统的推荐算法，以弥补单一推荐算法的缺点，以获得更好的推荐性能。基于融合的模型通过深度学习技术来学习更抽象，更深入的非线性交互特征，这使得模型的性能进一步提高。在MovieLens-1m数据集上的实验比较表明，提出的混合推荐模型可以显着提高收视率预测的准确性。