Serendipitous recommendation based on big context

Abstract

Context-awareness is an essential requirement in crafting recommender systems that provide serendipity, i.e. “pleasant surprises”, independently of human command. These solutions must be able to infer interactions based on data from sensors and recognised activities in order to infer what is useful information and when to deliver it. For that, we are devising advanced models of context inference based on the analysis of users’ signals during everyday activities. In this paper, we present a proof-of-concept platform that allows for the application of techniques of deep learning and context analytics to derive patterns in spatio-temporal context signals. We call this composition Big Context. We argue that by understanding how people and things are connected, one can devise novel forms of interactions that provide a more pleasant user experience. In this work, we introduce our method and platform, and illustrate someof the possible techniques using a prototype application that provides serendipitous recommendations.