College of Computer Science & Technology, Henan Institute of Technology, Xinxiang 453003, China.
Educational Technology Center, Guangzhou Railway Polytechnic, Guangzhou 510430, China.
Sensors (Basel). 2023 Jan 11;23(2):850. doi: 10.3390/s23020850.
Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system.
针对社交网络大数据中现有兴趣点 (POI) 推荐模型难以提取深度特征信息的问题，本文提出了一种基于社交网络和大数据的深度学习的 POI 推荐模型。输入数据均通过智能传感器采集，应用一些原始数据预处理任务，从而降低模型的计算负担。首先，设计了一种基于对称矩阵分解的 POI 静态特征提取方法，以捕获位置社交网络 (LBSN) 中的地理位置和 POI 类别特征。其次，改进的连续词袋 (CBOW) 模型用于提取用户评论信息中的语义特征，并实现 POI 在地理、类别、语义和时间特征空间中的隐式向量表示。最后，通过自适应选择签到历史中的相关签到活动来学习和改变用户偏好，地理时空门控循环单元网络 (GSGRUN) 可以区分不同签到的用户偏好。实验表明，在推荐列表长度为 15 的情况下，所提出的算法在 loc-Gowalla 数据集上的精度为 0.0686，召回率为 0.0769，在 loc-Brightkite 数据集上的精度为 0.0659，召回率为 0.0835，均优于对比推荐方法。因此，与对比方法相比，所提出的方法可以显著提高 POI 推荐系统的性能。
