TokenMinds extends Semantic ID tokenization from items to users, producing paired discrete tokens and dense embeddings via an LLM-adapted encoder-decoder for industrial recommendation.
Hymirec: A hybrid multi-interest learning framework for llm-based sequential recommendation
4 Pith papers cite this work. Polarity classification is still indexing.
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RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.
Pro-GEO introduces a geo-centroid coordinate system and geo-rotary position encoding to model geographic proximity as rotational transformations, enabling balanced semantic-spatial modeling in local service recommendations.
Token Factory maps dense, sparse, and sequence features into learned soft tokens, cutting LRM prompt length by ~70% while keeping ranking quality on par and improving retrieval of fresh videos.
citing papers explorer
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TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems
TokenMinds extends Semantic ID tokenization from items to users, producing paired discrete tokens and dense embeddings via an LLM-adapted encoder-decoder for industrial recommendation.
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RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
RRCM trains an LLM to dynamically retrieve from collaborative and meta memories using group relative policy optimization driven by final top-k recommendation quality.
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Birds of a Feather Cluster Nearby: a Proximity-Aware Geo-Codebook for Local Service Recommendation
Pro-GEO introduces a geo-centroid coordinate system and geo-rotary position encoding to model geographic proximity as rotational transformations, enabling balanced semantic-spatial modeling in local service recommendations.
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Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models
Token Factory maps dense, sparse, and sequence features into learned soft tokens, cutting LRM prompt length by ~70% while keeping ranking quality on par and improving retrieval of fresh videos.