Extracting Fast and Slow: User-Action Embedding with Inter-temporal Information (2206.09535v1)

Abstract: With the recent development of technology, data on detailed human temporal behaviors has become available. Many methods have been proposed to mine those human dynamic behavior data and revealed valuable insights for research and businesses. However, most methods analyze only sequence of actions and do not study the inter-temporal information such as the time intervals between actions in a holistic manner. While actions and action time intervals are interdependent, it is challenging to integrate them because they have different natures: time and action. To overcome this challenge, we propose a unified method that analyzes user actions with intertemporal information (time interval). We simultaneously embed the user's action sequence and its time intervals to obtain a low-dimensional representation of the action along with intertemporal information. The paper demonstrates that the proposed method enables us to characterize user actions in terms of temporal context, using three real-world data sets. This paper demonstrates that explicit modeling of action sequences and inter-temporal user behavior information enable successful interpretable analysis.