Gligorijevic, Dj., et. al. @ SIGIR 2018
Rapid expansion of mobile devices has brought an unprecedented opportunity for mobile operators and content publishers to reach many users at any point in time. Understanding usage patterns of mobile applications (apps) is an integral task that precedes advertising efforts of providing relevant recommendations to users. However, this task can be very arduous due to the unstructured nature of app data, with sparseness in available information. This study proposes a novel approach to learn representations of mobile user actions using Deep Memory Networks. We validate the proposed approach on millions of app usage sessions built from large scale feeds of mobile app events and mobile purchase receipts. The empirical study demonstrates that the proposed approach performed better compared to several competitive baselines in terms of recommendation precision quality. To the best of our knowledge this is the first study analyzing app usage patterns for purchase recommendation.
Gligorijevic, Dj., Gligorijevic, J., Raghuveer, A., Grbovic, M., Obradovic, Z. (2018) “ Modeling Mobile User Actions for Purchase Recommendations using Deep Memory Networks,” Proc. 41st Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR 2018), Ann Arbor, MI, July 2018.