Scaling model capacity has emerged as an effective approach to overcoming performance bottlenecks in industrial recommender systems. However, repeatedly training larger dense models from scratch demands substantial data and time, while their growing computation conflicts with the strict serving budgets of industrial sy...
Rui-Hao Zhang, Bo Chen, Xiao Wang et al.· 0 citations
Click-through rate (CTR) prediction is a fundamental task in industrial recommender systems. Cross-domain CTR prediction, which leverages data from a source domain to improve performance in a target domain, has emerged as a key strategy. However, most existing methods rely on overlapping users or items across domains t...
Jing-Yang Bin, Xing Tang, Wei Zeng et al.· Proceedings of the 20th ACM...· 0 citations
CoVeMem (Collaborative Vector Memory) is proposed, which vectorizes the collaborative core of the agent's memory and takes gradients: the full interaction history, out of reach for text, becomes available as training data for what the agent remembers and for how it reads what it remembers.
Hanting Chen, Xing Tang, Ling-Jie Li et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
