Personalized Recommender Systems in e-Commerce and m-Commerce: A Comparative Study

It is apparent that m-commerce and e-commerce have various similarities from operational and services perspectives. However, at the same time, m-commerce has its own a unique technology driven business opportunities with its own unique characteristics, functions, opportunities and challenges. One successful application in e-commerce is personalized recommendations services as results of recommender systems. Personalized recommender systems are emerging in m-commerce. This paper compares and contrasts ecommerce and m-commerce personalized recommender systems in B2C with the objective of finding what additional requirements are needed to adapt the models, methods and techniques developed and advanced in e-commerce for m-commerce. Various similarities and differences are identified and discussed along the dimensions user model, product and service model, recommender engine/algorithms, user interface (I/O and interaction), and confidence and uncertainty model, that make up a personalized recommender system. The two most prominent e-commerce and m-commerce personalized recommender systems of Amazon and MovieLens are discussed. The uncertainty and confidence measures in e-commerce and m-commerce personalized recommender systems are discussed further along with their potential for creating accurate mental model of the recommender system and its processes for the customers. Furthermore, the results from related research using fuzzy set and possibility theory for handling uncertainty in e-commerce showed a great potential for m-commerce. Two of the reasons for this potential are its high recommendation accuracy with a few numbers of recommendations; and its low latency.