CTS: Combine Temporal Influence and Spatial Influence for Time-Aware POI Recommendation

• 作者: Hanbing Zhang Yan Yang Zhaogong Zhang
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: RECOMMENDATION Point-of-interest Location-based social network Collaborative filtering SPATIO-TEMPORAL
• 年，卷（期）: 2016,（1）
• 页码范围: 71-73
• 页数: 3页
• 分类号: C5

摘要

As location-based social network (LBSN) services become more popular in people’s lives, Point of Interest (POI) recommendation has become an important research topic.POI recommendation is to recommend places where users have not visited before. There are two problems in POI recommendation: sparsity and precision. Most users only check-in a few POIs in an LBSN. To tackle the sparse problem in a certain extent, we compute the similarity between the check-in datasets of different times. For the precision problem, we incorporate temporal information and geographical information. The temporal information will influence how the user chooses and allow the user to visit different distance point on different day. The geographical information is also used as a control for points which are too far away from the user’s check-in data. Our experimental results on real life LBSN datasets show that the proposed approach outperforms the other POI recommendation methods substantially.