Does the Long Tail of Context Exist and Matter? The Case of Dialogue-based Recommender Systems

Context has been an important topic in recommender systems over the past two decades. Most of the prior CARS papers 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. In this paper, we study "context-rich" applications dealing with a large variety of different types of contexts. We demonstrate that supporting only a few of the most important contextual variables that could be manually identified, although useful, is not sufficient. In particular, we develop an approach to extract a large number of contextual variables for the dialogue-based recommender systems. In our study, we processed dialogues of bank managers with their clients and managed to identify over two hundred types of contextual variables forming the Long Tail of Context (LTC). We empirically demonstrate that LTC matters, and using all these contextual variables from the Long Tail leads to better recommendation performance.