  One of the most critical tasks of Microsoft sellers is to meticulously track
and nurture potential business opportunities through proactive engagement and
tailored solutions. Recommender systems play a central role to help sellers
achieve their goals. In this paper, we present a content recommendation model
which surfaces various types of content (technical documentation, comparison
with competitor products, customer success stories etc.) that sellers can share
with their customers or use for their own self-learning. The model operates at
the opportunity level which is the lowest possible granularity and the most
relevant one for sellers. It is based on semantic matching between metadata
from the contents and carefully selected attributes of the opportunities.
Considering the volume of seller-managed opportunities in organizations such as
Microsoft, we show how to perform efficient semantic matching over a very large
number of opportunity-content combinations. The main challenge is to ensure
that the top-5 relevant contents for each opportunity are recommended out of a
total of $\approx 40,000$ published contents. We achieve this target through an
extensive comparison of different model architectures and feature selection.
Finally, we further examine the quality of the recommendations in a
quantitative manner using a combination of human domain experts as well as by
using the recently proposed "LLM as a judge" framework.
