The implicit network inferred from users’ residences and workplaces enhancing collaborative recommendation on smartphones

Personalized recommendation based on side information extracted from social networks has achieved promising performance in numerous applications. However, such side information is generally derived from users’ explicit interactions, such as Twitter connections or trust lists, which is not always available in most scenarios. Alternately, obtaining such side information from users’ implicit networks is easy. Thus, in this paper, we consider the similarity network for users’ semantic locations, assuming that users with neighboring semantic locations have similar consumption habits. To demonstrate this, we evaluate our study in a practical scenario: the smartphone recommendation based on operator records. A novel recommendation paradigm is designed, which includes three key steps: discovery of semantic locations, hierarchical construction of the neighbor network, and items’ popularity-based recommendation based on interpersonal similarities. The empirical results illustrate that our method outperforms the state-of-the-art methods (13% coverage improvement than models without introducing networks and about 5% higher than model with a call-log based network).