面向稀疏数据场景的生成对抗网络推荐算法

A generative adversarial network recommendation algorithm for sparse data scenarios

提出一个改进的基于生成对抗网络的协同过滤(CFGAN)的模型,通过引入增强的置换注意力机制,强化其面向稀疏数据集的特征聚焦能力,并考虑用户可能交互物品对推荐结果的影响.此外,将协同用户社交网络从用户反馈中提取的语义好友特征嵌入CFGAN,以实现负样本的个性化抽取,进一步提升模型面向稀疏数据场景的推荐效果.

An improved collaborative filtering framework based on generative adversarial network(CFGAN)model is proposed to enhance the feature focus capability for sparse datasets by introducing an enhanced shuffle attention mechanism,while taking into account the influence of possible user interaction items on the recommendation results.Furthermore,the semantic friend features extracted from user feedback by collaborative user social networks are embedded into CFGAN to achieve personalized extraction of negative samples,further enhancing the model’s recommendation effectiveness for sparse data scenarios.