A general framework for the implementation of context-aware recommendation engines for mobile applications
Modern e-commerce systems offer a multitude of products and services in global marketplaces. The modern consumer is therefore overwhelmed by millions of options, variants and choices of products and services. With the rise of global marketplaces with their huge amount of items, recommendation systems became the basis for modern e-commerce systems. The traditional approaches for implementing recommendation engines, such as content and collaborative l- tering, solve the challenge of calculating a recommendation set of items for a given user. While these traditional approaches cope well with large sets of static user and item information, they lack a general approach for including highly dynamic context-information. As the e-commerce market swiftly changes to mobile computing platforms, such as smart-phones and tablets, the use of context-information for generating item recommendations is of great interest. Within this work we propose a concept for a general framework for the implementation of such context-aware recommendation engines, specically for mobile platforms.