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.

中文翻译：

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BI-REC：会话式商务智能的指导数据分析

与商务智能（BI）应用程序的会话接口使您可以使用自然语言对话框以较小的增量步骤进行数据分析。为了真正释放对话式BI的能力以使对数据的访问民主化，系统需要为数据分析提供有效且持续的支持。在本文中，我们提出了BI-REC，这是一种针对BI应用程序的会话推荐系统，可以帮助用户完成其数据分析任务。我们根据BI模式定义数据分析的空间，并使用从OLAP多维数据集定义中提取的丰富语义信息进行扩充，并使用通过GraphSAGE学习的图形嵌入来创建分析状态的紧凑表示形式。我们提出了一种两步方法来探索有用的BI模式建议的搜索空间。在第一步中 我们使用先前的查询日志来训练多类分类器，以根据BI操作（例如{\ em Drill-Down}或{\ em Roll-up}）以及用户的衡量标准来预测下一个高级操作感兴趣。在第二步中，使用协作过滤将高级操作进一步细化为实际的BI模式建议。这种两步方法不仅使我们能够划分和征服巨大的搜索空间，而且需要的训练数据也更少。我们的实验评估表明，与最新的基准相比，BI-REC对于BI模式建议的准确性达到了83％，预测延迟的速度提高了2倍。我们的用户研究进一步表明，BI-REC在多项不同的分析任务中提供的精度@ 3为91.90％的建议。