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Citations since 2017
Introduction
Publications
Publications (3)
A remarkable success in recommendations has been achieved by using methods based on metric learning, especially in digital marketing. However, the existing methods do not consider the relative preferences among items that users like. To overcome this issue, we propose an improved recommender model. First, the model analyses the user-item bipartite...
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 s...
Recently, mobile applications are widely used by smartphone owners. The understanding of application usage can help us make prediction on its development tendency and meanwhile improve users’ experience. To predict the future application usage, we develop a simple but novel method that considers two types of user-related networks (the users’ call-l...
Projects
Project (1)
