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This work proposes a novel memory framework, named MemoType, which can adaptively recognize each memory and query type with the learned router model, and proposes a novel retrieval framework, named MemoType, which can adaptively recognize each memory and query type with the learned router model.
Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user behavior via next-token prediction across diverse recommendation scenarios. Extending this paradigm to cross-domain recommendation is challengi...
Modern recommender systems advance not only by scaling data and parameters, but also by encoding task-specific inductive biases through architecture, including sparse feature interactions for click-through rate (CTR) prediction, temporal attention for sequential recommendation, and expert routing for multi-task learnin...
The effectiveness of Inherit4Rec for continual capacity expansion and computation-efficient sparse conversion in industrial recommender systems is demonstrated, and both transformations consistently outperform the evaluated inheritance baselines across all prediction objectives.
A lightweight training framework that learns a single Behavior-Equivalent Token that substantially reduces inference cost and frees nearly the entire context window for user inputs and model outputs.
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