2017/11/20 by Deguang Kong, Kong, Deguang · 8 citations
Computer Science · Social Sciences · #App store #Big data #Cloud computing #Computer science #Computers and Society (cs.CY) #Data Stream Mining Techniques #Data science #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Human–computer interaction #IoT and Edge/Fog Computing #Mobile device #Multimedia #Popularity #Profiling (computer programming) #Revenue #World Wide Web #cs.CY
paper · pdf · doi:10.48550/arxiv.1711.07580
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/11/20 · openalex publication_date 2017/11/20 · arxiv updated 2017/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Recent years have witnessed the increasing popularity of mobile devices (such as iphone) due to the convenience that it brings to human lives. On one hand, rich user profiling and behavior data (including per-app level, app-interaction level and system-interaction level) from heterogeneous information sources make it possible to provide much better services (such as recommendation, advertisement targeting) to customers, which further drives revenue from understanding users' behaviors and improving user' engagement. In order to delight the customers, intelligent personal assistants (such as Amazon Alexa, Google Home and Google Now) are highly desirable to provide real-time audio, video and image recognition, natural language understanding, comfortable user interaction interface, satisfactory recommendation and effective advertisement targeting. This paper presents the research efforts we have conducted on mobile devices which aim to provide much smarter and more convenient services by leveraging statistics and big data science, machine learning and deep learning, user modeling and marketing techniques to bring in significant user growth and user engagement and satisfactions (and happiness) on mobile devices. The developed new features are built at either cloud side or device side, harmonically working together to enhance the current service with the purpose of increasing users' happiness. We illustrate how we design these new features from system and algorithm perspective using different case studies, through which one can easily understand how science driven innovations help to provide much better service in technology and bring more revenue liftup in business. In the meantime, these research efforts have clear scientific contributions and published in top venues, which are playing more and more important roles for mobile AI products.