【摘要】 协同过滤作为一种常见的推荐系统实现方式,能给用户带来个性化的推荐服务体验。传统的协同过滤模型没有对用户和项目的不同显式属性的关注程度进行挖掘和分析,导致了不同显式属性的关键程度未被模型关注。因此,在基于卷积神经网络的耦合协同过滤模型的基础上,文中引入了注意力机制,以深度挖掘显式属性的关键程度,增强在关键属性上的参数学习梯度;提出了一种新的耦合程度计算方法,以保证参数的齐次性,提高模型的推荐性能。实验结果表明,文中提出的模型的推荐精准率较传统协同过滤方法和耦合协同过滤模型更优,top K@10命中率和归一化折损累计增益分别达到0.850 8与0.585 0。更多还原

【Abstract】 As a common implementation of recommender system, collaborative filtering can bring personalized recommendation service experience to users. Traditional collaborative filtering models do not mine and analyze the attention level of different explicit attributes of users and items, leading to the critical level of different explicit attributes not paid attention by the model. Therefore, based on the coupled collaborative filtering model based on convolutional neural network, an attention mechanism was introduced in the paper to deeply mine the critical degree of explicit attributes and enhance the parameter learning gradient on critical attributes. And a new method of calculating the coupling degree was proposed to ensure the flushness of parameters and to improve the recommendation performance of the model. The experimental results show that the recommendation accuracy rate of the model proposed in the paper is better than that of traditional collaborative filtering methods and coupled collaborative filtering models, and the cumulative gain of topK@10 hit ratio and normalized discount cumulative gain reach 0.850 8 and 0.585 0, respectively.更多还原