Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation

Recommendation systems quietly shape what billions of people watch, buy, and read. The latest frontier in this space is generative recommendation, which frames next-item prediction as a generation problem rather than a retrieval one. A core challenge is representing user behavior richly enough for a generative model to reason over it without drowning in noise or computational cost. G2Rec addresses this by combining graph-based modeling of user co-engagement patterns with semantically grounded tokenization. Practical applications include large-scale e-commerce and streaming platforms seeking more accurate, context-aware recommendations — particularly in cold-start or long-tail scenarios where behavioral signals are sparse but semantics carry weight.