Modelling User Behavior Dynamics with Embeddings

Abstract

Understanding user interaction behaviors remains a challenging problem. Quantifying behavior dynamics over time as users complete tasks has only been done in specific domains. In this paper, we present a user behavior model built using behavior embeddings to compare behaviors and their change over time. To this end, we first define the formal model and train the model using both action (e.g., copy/paste) embeddings and user interaction feature (e.g., length of the copied text) embeddings. Having obtained vector representations of user behaviors, we then define three measurements to model behavior dynamics over time, namely: behavior position, displacement, and velocity. To evaluate the proposed methodology, we use three real world datasets: (i) tens of users completing complex data curation tasks in a lab setting, (ii) hundreds of crowd workers completing structured tasks in a crowdsourcing setting, and (iii) thousands of editors completing unstructured editing tasks on Wikidata. Through these datasets, we show that the proposed methodology can: (i) surface behavioral differences among users; (ii) recognize relative behavioral changes; and (iii) discover directional deviations of user behaviors. Our approach can be used (i) to capture behavioral semantics from data in a consistent way, (ii) to quantify behavioral diversity for a task and among different users, and (iii) to explore the temporal behavior evolution with respect to various task properties (e.g., structure and difficulty).