TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
arXiv:2606.25147v1 Announce Type: cross Abstract: User modeling in industrial recommender systems typically produces dense embeddings, which suffer from representational constraints inherent to fixed-dimensional vectors. An emerging alternative for discrete user representation -- using LLMs to generate text-based user tokens -- captures topical co-occurrences rather than deep sequential behavior dynamics and produces outputs that are difficult to ground to item attributes. Meanwhile, Semantic ID (SID) based item tokenization has proven effective for improving generalization in generative recom
The increasing scale of recommender systems and the limitations of current user modeling techniques are driving innovation towards more sophisticated, interpretable, and scalable user representations.
This development represents a significant advancement in how large-scale AI systems understand and interact with individual users, potentially leading to a new standard in personalization and user experience.
The shift from dense embeddings and LLM-generated text tokens to pretrained, discrete user tokens (TokenMinds) could fundamentally alter the architecture and performance of recommender systems.
- · Large Recommender Systems Providers
- · E-commerce Platforms
- · Content Streaming Services
- · AI/ML Research Firms
- · Legacy User Modeling Techniques
- · Companies reliant on simple collaborative filtering
- · LLM-only based user profiling systems (if not integrated)
Recommender systems become significantly more accurate and efficient due to improved user understanding.
Enhanced personalization drives higher engagement and conversion rates across various digital platforms.
The development of highly granular and dynamic user profiles could raise new ethical and privacy considerations regarding data collection and usage.
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Read at arXiv cs.LG