Context-Aware Recommendation Via Interactive Conversational Agents: A Case in Business Analytics

In the era of information overload, the ability to access key information instantaneously is extremely important. While technological advances such as keyword search, dashboards, customizable data reports, and notifications have made information access more flexible, the underlying assumption is that the user knows what to look for. However, this assumption may not hold in many situations. For example, identifying needed information and key metrics affecting a business in Human Resource Management Systems (HRMS) can prove to be difficult. Voice assistance and recommendation systems can help improve these issues by allowing users to efficiently reach key insights which are relevant to their needs and their context. This research presents the design and evaluation of a conversational context-aware information recommendation system for business analytics where a conversational voice assistant helps the user specify the information needed for different analytics by suggesting reports and metrics often used by similar users and companies in their industry. Our prototype evaluation results show the potential of such a system to improve the user experience of searching for efficient and meaningful information in an organization using the data available within their HRMS.