Recent times have seen data analytics software applications become an
integral part of the decision-making process of analysts. The users of these
software applications generate a vast amount of unstructured log data. These
logs contain clues to the user's goals, which traditional recommender systems
may find difficult to model implicitly from the log data. With this assumption,
we would like to assist the analytics process of a user through command
recommendations. We categorize the commands into software and data categories
based on their purpose to fulfill the task at hand. On the premise that the
sequence of commands leading up to a data command is a good predictor of the
latter, we design, develop, and validate various sequence modeling techniques.
In this paper, we propose a framework to provide goal-driven data command
recommendations to the user by leveraging unstructured logs. We use the log
data of a web-based analytics software to train our neural network models and
quantify their performance, in comparison to relevant and competitive
baselines. We propose a custom loss function to tailor the recommended data
commands according to the goal information provided exogenously. We also
propose an evaluation metric that captures the degree of goal orientation of
the recommendations. We demonstrate the promise of our approach by evaluating
the models with the proposed metric and showcasing the robustness of our models
in the case of adversarial examples, where the user activity is misaligned with
selected goal, through offline evaluation.Comment: 14th ACM Conference on Recommender Systems (RecSys 2020