基于实时事件侦测的兴趣点推荐系统研究

Recommending Point-of-Interests with Real-Time Event Detection

【目的】结合实时事件、合适时机与兴趣点特性三个要素,建立一个基于实时事件侦测的兴趣点推荐系统。【方法】从大量具有地理标记的推文中侦测出实时事件,通过树状卷积神经网络来学习实时事件与时间感知信息的嵌入特征表示;从标注在兴趣点的文字评论与照片中抓取兴趣点的图文内容感知特征,并通过卷积神经网络学习兴趣点的图文特征向量;使用前K处召回率与排名倒数平均值两种度量指标,通过实验数据比较和评估不同推荐系统的效能。【结果】所提模型在排名倒数平均值(MRR)评估项目的推荐效能上比MP推荐模型提升8.9%,比NMF推荐模型提升57.1%。【局限】兴趣点固有特征仅考虑文字和图像特征,未考虑其他信息。【结论】所提基于实时事件侦测的兴趣点推荐模型比其他推荐方法具有更好的效果,在搜寻、运输和环境监控等基于位置的推荐服务中具有广阔的应用前景。

[Objective]This paper constructs a point-of-interest(POI)recommendation system based on real-time event detection,appropriate time and POI characteristics.[Methods]First,we retrieved the real-time events from a large number of tweets with geographical markers.Then,the system learned the embedded feature representation of real-time events and time perception information through tree convolution neural network.Third,we captured the perceptual features of POI’s graphic contents from comments and photos.Fourth,the system learned the graphic feature vector of POI with convolution neural network.Finally,we used the recall rate at the top K and the average of the reciprocal of the ranking to evaluate the effectiveness of different recommendation systems.[Results]The mean reciprocal rank(MRR)of the proposed model is 8.9%higher than that of the MP model and 57.9%higher than that of the non-negative matrix factorization(NMF)model.[Limitations]The characteristics of POI only include textual and image features,which need to be expanded.[Conclusions]The proposed model could effectively recommend point-of-interests,which benefits location-based services such as search,transportation and environmental monitoring.