关键词: 深度学习, 神经网络, 隐性反馈, 注意力机制, 协同过滤

Abstract: Since the traditional item-based collaborative filtering algorithms only consider the score of historical items when calculating the similarity between items, but they ignore the impact of historical item preferences, therefore, the recommended accuracy is not ideal. To solve this issue, this paper proposes a movie recommendation algorithm combining deep learning and attention mechanism. Firstly based on the implicit feedback obtained, on the feature-level attention frame, starting from the item content feature extraction network, the preference of item features is learned. Then the item feature preferences and project features are weighted to obtain the project content feature vector. Finally, in the item-level feature attention frame, it obtains the final recommendation results through the scores of the item preferences learned by the item content feature vector. The experimental results on MovieLens 100K and MovieLens 1M datasets demonstrate that the proposed algorithm has higher accuracy and recommendation personalization than the traditional algorithms and outperforms the state-of-the-art methods.

Key words: deep learning, neural networks, implicit feedback, attention mechanism, collaborative filtering