Welcome to our latest episode where we dive into TIMeSynC, a groundbreaking framework developed by researchers at Capital One to revolutionise intent prediction in financial services. Managing customer journeys across mobile apps, call centres, and web platforms is historically difficult because data is recorded at vastly different temporal resolutions.

The novelty of TIMeSynC lies in its encoder-decoder transformer architecture, which employs ALiBi-based time representations and synchronised context encodings to align these heterogeneous data streams. By flattening multi-channel activity into a single tokenised sequence, it eliminates the need for hours of manual feature engineering, allowing the model to learn complex temporal patterns directly.

In terms of applications, this technology enables highly personalised digital experiences, such as contextual chatbot Q&A, targeted marketing, and predicting a user’s "next best action"—whether that is redeeming rewards or reporting fraud. However, a notable limitation is that flattening data across domains can lead to an "explosion" of the encoder context window, and the results may not yet generalise to datasets with different characteristics. Join us as we explore how TIMeSynC significantly outperforms traditional tabular methods to set a new standard in sequential recommendation.

Paper link: https://arxiv.org/pdf/2410.12825