BI-REC: Guided Data Analysis for Conversational Business Intelligence

TL;DR

BI-REC is a novel conversational recommendation system that enhances data analysis in Business Intelligence applications by predicting and suggesting relevant BI patterns, thereby improving user support and efficiency.

Contribution

It introduces a two-step approach combining classification and collaborative filtering for effective BI pattern recommendations in conversational BI systems.

Findings

Achieves 83% accuracy in BI pattern recommendations.

Provides up to 2X faster prediction latency.

Attains 91.90% precision@3 in user studies.

Abstract

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.