Stuck? No worries!: Task-aware Command Recommendation and Proactive Help for Analysts
This addresses a domain-specific problem for data analysts using analytics software, offering incremental improvements through task-aware recommendations.
The authors tackled the problem of analysts struggling with data analytics software due to insufficient knowledge by proposing a task-aware command recommendation system that proactively provides help. They demonstrated superior performance of their neural models using log data from a web-based analytics software, though no specific numbers were provided.
Data analytics software applications have become an integral part of the decision-making process of analysts. Users of such a software face challenges due to insufficient product and domain knowledge, and find themselves in need of help. To alleviate this, we propose a task-aware command recommendation system, to guide the user on what commands could be executed next. We rely on topic modeling techniques to incorporate information about user's task into our models. We also present a help prediction model to detect if a user is in need of help, in which case the system proactively provides the aforementioned command recommendations. We leverage the log data of a web-based analytics software to quantify the superior performance of our neural models, in comparison to competitive baselines.