融合时间感知与兴趣偏好的推荐模型研究

Recommendation Model Based on Time Aware and Interest Preference

针对传统的推荐模型无法挖掘用户细粒度兴趣偏好的问题,提出了一种融合时间感知与兴趣偏好的推荐模型(TAIP)。在TAIP模型中,将用户交互的时间间隔信息作为辅助信息引入到序列嵌入矩阵中,并设计多尺度时序卷积网络与通道和空间注意力机制精准地提取细粒度短期偏好,同时采用Transformer编码器挖掘目标项目与用户兴趣之间的长期偏好,最后利用全连接网络实现全局特征融合提供推荐。在公开数据集MovieLens-1M和YELP上进行实验,实验结果表明TAIP模型在HR、NDCG和MRR评价指标上相较于其他模型至少提升了4.84%和1.38%,具有更佳的推荐性能,验证了TAIP模型的有效性。

To address the problem that traditional recommendation models cannot mine users’fine-grained interest preferences,a recommendation model based on time aware and interest preferences(TAIP)is proposed.In TAIP model,the time interval information of user interaction is introduced into the sequence embedding matrix as auxiliary information,and a multi-scale temporal convolutional network with channel and spatial attention mechanisms is designed to accurately extract fine-grained short-term preferences.At the same time,the Transformer encoder is used to mine long-term preferences between target items and user interests.Finally,a fully connected network is used to achieve global feature fusion to provide recommendations.Experiments are conducted on the publicly available datasets MovieLens-1M and YELP.Compared with other models,TAIP model improves at least 4.84%and 1.38%in HR,NDCG and MRR,which has better recommendation performance and verifies the effectiveness of TAIP model.