arXiv:2502.10205v3 Announce Type: replace Abstract: Representation learning produces models in different domains, such as store purchases, client transactions, and general people's behavior. However, such models for event sequences usually process each sequence in isolation, ignoring context from those that co-occur in time. This limitation is particularly problematic in domains with fast-evolving conditions, like finance and e-commerce, or when certain sequences lack recent events. We develop a method that aggregates information from multiple user representations, augmenting a specific user's
The continuous drive for more sophisticated AI models and representation learning, coupled with increasing data complexity, necessitates innovations such as incorporating external contextual information for better performance.
Improving representation learning by integrating external context significantly enhances the accuracy and applicability of AI models across critical domains like finance, e-commerce, and general behavioral prediction.
AI models for event sequences will move from isolated processing to context-aware systems, leading to more robust and adaptive predictions even in fast-evolving or data-sparse environments.
- · AI/ML research and development
- · Financial institutions
- · E-commerce platforms
- · Personalized services
- · Traditional isolated representation models
- · Companies relying on simplistic behavioral models
Enhanced AI models will provide more accurate and timely insights into user behavior and market dynamics.
This improved understanding could lead to more effective automated decision-making in various industries, from credit scoring to targeted marketing.
The ability to integrate vast external data for representation learning foundational to the development of more complex and autonomous AI agents.
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Read at arXiv cs.LG