Internet users would like to obtain interesting location information for a travel. With the rapid development of social media, many kinds of location recommender systems are proposed in recent years. Existing methods mostly focus on mining user check-in information that could be leveraged to understand their trajectories. However, the characteristics and attributes of geographical locations also play an important role in recommender systems. In this paper, sentimental attributes of locations are explored and we propose a Point of Interest (POI) mining method and a personalized recommendation model by fusing sentimental spatial context. First, a Sentimental-Spatial POI Mining (SPM) method is utilized to mine the POIs by fusing the sentimental and geographical attributes of locations. Second, we recommend the POIs to users by a Sentimental-Spatial POI Recommendation (SPR) model incorporating the factors of sentiment similarity and geographical distance. Last, the advantages and superior performance of our methods are demonstrated by extensive experiments on a real-world dataset.

互联网用户希望获得旅行的有趣位置信息。随着社交媒体的迅速发展，近年来提出了多种位置推荐系统。现有方法主要集中在挖掘用户签到信息上，这些信息可用于理解他们的轨迹。但是，地理位置的特征和属性在推荐系统中也起着重要作用。本文探讨了位置的情感属性，并通过融合情感空间上下文提出了兴趣点（POI）挖掘方法和个性化推荐模型。首先，采用情感空间POI挖掘（SPM）方法通过融合位置的情感和地理属性来挖掘POI。第二，我们通过情感空间POI推荐（SPR）模型向用户推荐POI，该模型结合了情感相似性和地理距离的因素。最后，通过在真实数据集上进行的大量实验证明了我们方法的优点和优越的性能。