  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.
