The Contextual Turn

From Context-Aware to Context-Driven Recommender Systems

Conference paper (2016)

Authors

R. Pagano Politecnico di Milano
Paolo Cremonesi Politecnico di Milano
M.A. Larson Radboud Universiteit Nijmegen,
Balázs Hidasi Gravity Research
Domonkos Tikk Gravity Research
Alexandros Karatzoglou Telefonica I+D
Massimo Quadrana Politecnico di Milano
Recommender systems Context Position paper
More Info
expand_more

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

A critical change has occurred in the status of context in recommender systems. In the past, context has been considered 'additional evidence'. This past picture is at odds with many present application domains, where user and item information is scarce. Such domains face continuous cold start conditions and must exploit session rather than user information. In this paper, we describe the `Contextual Turn?: the move towards context-driven recommendation algorithms for which context is critical, rather than additional. We cover application domains, algorithms that promise to address the challenges of context-driven recommendation, and the steps that the community has taken to tackle context-driven problems. Our goal is to point out the commonalities of context-driven problems, and urge the community to address the overarching challenges that context-driven recommendation poses.