DeepInterestGR: Mining Deep Multi-Interest Using Multi-Modal LLMs for Generative Recommendation
arXiv:2602.18907v2 Announce Type: replace Abstract: We introduce DeepInterestGR, a novel framework that integrates deep interest mining into the generative recommendation pipeline. This addresses the "Shallow Interest" problem - existing generative methods rely on surface-level textual features and fail to capture latent user motivations, limiting personalization depth and recommendation interpretability. Our approach leverages Multi-LLM Interest Mining (MLIM) via structured reasoning prompting, Reward-Labeled Deep Interest (RLDI) for quality control, and Interest-Enhanced Item Discretization
The increasing sophistication of large language models is enabling more nuanced approaches to personalized recommendations, addressing previous limitations in capturing latent user interests.
Improving generative recommendation systems through deep interest mining can significantly enhance personalization, drive consumption, and increase engagement across various digital platforms.
Recommendation systems can move beyond surface-level textual features to infer deeper user motivations, leading to more relevant and interpretable suggestions.
- · E-commerce platforms
- · Content streaming services
- · AI developers
- · Digital advertising
- · Generic recommendation engines
- · Users with shallow preferences
More accurate and personalized recommendations lead to higher user satisfaction and conversion rates.
Enhanced personalization could create filter bubbles or reinforce existing biases, requiring careful ethical consideration in system design.
The ability to deeply understand and predict user interests could become a critical competitive advantage, further concentrating market power among companies with superior AI capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG