南京大学学报（自然科学版）2024，Vol.60Issue(3)：429-441,13.DOI:10.13232/j.cnki.jnju.2024.03.007
结合兴趣点类别周期属性和用户短期偏好特征的推荐模型
A recommendation model combining point of interest category periodic attributes and user short-term preference features
摘要
Abstract
With the widespread application of location-based social networks in daily life,how to effectively extract users'hidden interests and behavior sequence patterns,and provide users with the next Point of Interest(POI)recommendation service to meet their personalized needs has become one of the hot issues in the recommendation field.Aiming at the problem of user preference mining in the next POI recommendation,this paper proposes a POI recommendation model CPSTIN(Combining Periodic and Spatio-Temporal Intervals'Network)based on the combination of periodic preference of user POI category and short-term interest.The model embeds the user record of signing in into the time window by hour period pattern,and uses the multi-head self-attention mechanism to extract the the user's periodic preference combined with the category of POI.At the same time,the model sends the discontinuous spatio-temporal interval information into the learnable matrix,and uses the linear interpolation method to extract the user's short-term interest based on high-order correlation.Finally,the validity of the model is verified on two real datasets.The model effectively uses the user's high-order relevance short-term interest and the periodic preference based on the POI category to more accurately predict the next POI that the user is most likely to visit.关键词
兴趣点推荐/自注意力机制/线性插值嵌入/类别周期兴趣Key words
POI recommendation/self-attention/linear interpolation embedding/category periodic interest分类
信息技术与安全科学引用本文复制引用
桑春艳,易星宇,廖世根,文俊浩..结合兴趣点类别周期属性和用户短期偏好特征的推荐模型[J].南京大学学报（自然科学版）,2024,60(3):429-441,13.基金项目
国家自然科学基金(62002037,620720060),重庆市自然科学基金(cstc2019jcyj-msxmX0588) （62002037,620720060）
