Abstract
As a user can only visit a limited number of venues/events and most of them are within a limited distance range, the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this chapter, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people’s travel not only near the area in which they live, but also in a city that is new to them. We evaluate the performance of our recommender system on two large-scale real datasets, DoubanEvent, and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities.
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Yin, H., Cui, B. (2016). Spatial Context-Aware Recommendation. In: Spatio-Temporal Recommendation in Social Media. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-0748-4_3
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DOI: https://doi.org/10.1007/978-981-10-0748-4_3
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