Abstract
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.
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Zhang, H., Yang, Y., Zhang, Z. (2016). CTS: Combine Temporal Influence and Spatial Influence for Time-Aware POI Recommendation. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_25
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DOI: https://doi.org/10.1007/978-981-10-2053-7_25
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