The Long Tail of Context: Does it Exist and Matter?

TL;DR

This paper investigates the significance of the Long Tail of Context (LTC) in complex recommender systems, showing that leveraging a broad spectrum of over two hundred contextual variables improves recommendation accuracy.

Contribution

It introduces the concept of the Long Tail of Context in recommender systems and empirically demonstrates its importance in enhancing recommendation performance.

Findings

Using all contextual variables from the LTC improves recommendation accuracy.

Supporting only a few key contextual variables is insufficient in context-rich applications.

The LTC concept helps in understanding the broad spectrum of contexts affecting recommendations.

Abstract

Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their structures are known in an application. Most of the prior CARS papers following representational approach manually selected and considered only a few crucial contextual variables in an application, such as time, location, and company of a person. This prior work demonstrated significant recommendation performance improvements when various CARS-based methods have been deployed in numerous applications. However, some recommender systems applications deal with a much bigger and broader types of contexts, and manually identifying and capturing a few contextual variables is not sufficient in such cases. In this paper, we study such ``context-rich'' applications dealing with a large variety of different types of contexts. We demonstrate that supporting only a few most important contextual variables, although useful, is not sufficient. In our study, we focus on the application that recommends various banking products to commercial customers within the context of dialogues initiated by customer service representatives. In this application, we managed to identify over two hundred types of contextual variables. Sorting those variables by their importance forms the Long Tail of Context (LTC). In this paper, we empirically demonstrate that LTC matters and using all these contextual variables from the Long Tail leads to significant improvements in recommendation performance.