  Traditionally, assets are selected for inclusion in a portfolio (long or
short) by human analysts. Teams of human portfolio managers (PMs) seek to weigh
and balance these securities using optimisation methods and other portfolio
construction processes. Often, human PMs consider human analyst recommendations
against the backdrop of the analyst's recommendation track record and the
applicability of the analyst to the recommendation they provide. Many firms
regularly ask analysts to provide a "conviction" level on their
recommendations. In the eyes of PMs, understanding a human analyst's track
record has typically come down to basic spread sheet tabulation or, at best, a
"virtual portfolio" paper trading book to keep track of results of
recommendations.
  Analysts' conviction around their recommendations and their "paper trading"
track record are two crucial workflow components between analysts and portfolio
construction. Many human PMs may not even appreciate that they factor these
data points into their decision-making logic. This chapter explores how
Artificial Intelligence (AI) can be used to replicate these two steps and
bridge the gap between AI data analytics and AI-based portfolio construction
methods. This field of AI is referred to as Recommender Systems (RS). This
chapter will further explore what metadata that RS systems functionally supply
to downstream systems and their features.
