  Conversational interfaces to Business Intelligence (BI) applications enable
data analysis using a natural language dialog in small incremental steps. To
truly unleash the power of conversational BI to democratize access to data, a
system needs to provide effective and continuous support for data analysis. In
this paper, we propose BI-REC, a conversational recommendation system for BI
applications to help users accomplish their data analysis tasks.
  We define the space of data analysis in terms of BI patterns, augmented with
rich semantic information extracted from the OLAP cube definition, and use
graph embeddings learned using GraphSAGE to create a compact representation of
the analysis state. We propose a two-step approach to explore the search space
for useful BI pattern recommendations. In the first step, we train a
multi-class classifier using prior query logs to predict the next high-level
actions in terms of a BI operation (e.g., {\em Drill-Down} or {\em Roll-up})
and a measure that the user is interested in. In the second step, the
high-level actions are further refined into actual BI pattern recommendations
using collaborative filtering. This two-step approach allows us to not only
divide and conquer the huge search space, but also requires less training data.
Our experimental evaluation shows that BI-REC achieves an accuracy of 83% for
BI pattern recommendations and up to 2X speedup in latency of prediction
compared to a state-of-the-art baseline. Our user study further shows that
BI-REC provides recommendations with a precision@3 of 91.90% across several
different analysis tasks.
