Context-aware recommender systems

TLDR

Contextual information is increasingly recognized as crucial across domains, yet most recommender systems still ignore factors such as time, location, or social context. This chapter argues that incorporating contextual data is essential for delivering relevant recommendations. We outline how context can be modeled and present three algorithmic paradigms—prefiltering, post-filtering, and modeling—along with a case study of a combined approach. We highlight additional capabilities of context‑aware recommenders and outline promising future research directions.

Abstract

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.