[Research Seminar 2018.10.26]Behavior-based Recommender Systems using Big Data AnalysisSpeaker : Ki Hwan Nam(Collegiate Faculty of SBA,UNIST)
This research proposal is to present effective online targeted marketing strategies using big data of user online behavior analysis. In study 1, user’s online mobile shopping behavior includes channel of influx, duration of use, usage date, device type, and shopping mall type. Heterogeneous characteristics for both individual behavior and group behavior showed disparity on various circumstances and statistically confirmed by econometric models. In study 2, statistically significant variables of online shopping behavior such as marketing channel, device type, and login time for both individual and group heterogeneity was applied to develop a purchase probability prediction model and to design the recommender system algorithm for targeted marketing. User preference change based on behavior has been critical limitation from conventional recommender systems, therefore, this research proposal is to resolve the limitation by the designed behavior pattern algorithm developed from online user data. The recommender system performance was significantly improved by an algorithm for big data analytics for the recommender system and an econometric model based on a theoretical approach and empirical analysis. In study 3, data mining technique was applied to solve the limitation from conventional recommender systems, such as computational complexity problem from the big data. Moreover, an algorithm was developed from repetitive tests and development from conventional recommender systems. Introducing a generalized algorithm model from the implications of simulation results, a theoretical framework was proposed to make use of the generalized model in the online shopping industry. This research analyzed the online shopping industry big data to propose an integration of econometric models and data mining techniques. This research not only make on academic contribution on marketing field but also contributes to the shopping industry by practical usability of a developed online shopping algorithm.
Keywords: behavior-based recommendation, recommender system, big-data analytics, mobile channel, mobile marketing, context-aware, data mining, hierarchical Bayesian methods, deep learning