Collaborative Spatial Object Recommendation in Location Based Services

Recommendation systems have found their ways into many on-line web applications, e.g., product recommendation on Amazon and movie recommendation on Netflix. Particularly, collaborative filtering techniques have been widely used in these systems to personalize the recommendations according to the needs and tastes of users. In this paper, we apply collaborative filtering in spatial object recommendation which is essential in many location based services. Due to the large number of spatial objects and participating users, using collaborative filtering to obtain recommendations for a particular user can be very expensive. However, we observe that users tend to have affinity for some regions and argue that using users with similar regional bias in recommendation may help in reducing the search space of similar users. Thus, we propose two techniques, namely, Access Minimum Bounding Rectangle Overlapped Area (AMBROA) and Grid Division Cosine Similarity (GDCS), to form regions of interests that represent user location interests and activities and to find users with local access similarity to facilitate effective spatial object recommendation. We conduct an extensive performance evaluation to validate our ideas. Evaluation result demonstrates the superiority of our proposal over the conventional approach.