Sequential recommendation aims to model users' evolving preferences from their historical interactions. Recent latent reasoning methods improve sequential recommenders by extending inferencetime computation in continuous representation space, refining user representations without generating explicit reasoning text. How...
Minxuan Shan, Shu-Xian Bi, Zi-Ye Zhang et al.· 1 citation
A self-evolving memory paradigm for generative recommendation is proposed, aiming to enable effective evolution across heterogeneous behavioral patterns, and three key principles for effective self-evolving recommendation systems are identified, including isolated memorization, reinforced evolution, and scalable applic...
Xin-Yu Lin, Zhuo-Song Jiang, Zi-Xiao Suo et al.· 0 citations
A novel set identifier paradigm is introduced, representing each item as a set of order-agnostic tokens, which proves SETRec’s superior efficiency and scalability on cold-start items as model sizes increase, and SETRec++’s potential in the scalability of order-agnostic identifier.
Xin-Yu Lin, Chuan-Bo Zhang, Yu-Fan Liu et al.· ACM Transactions on Recommen...· 0 citations
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