394 citations · 400 across the 5 of their papers we have counts for
5 papers
UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
Rongcheng Lin, Yan Sun, Jamey Zhang +4
Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models ov…
Memory Layer: Train the In-Model Cache for Recommendation Models
Liangyuan Na, Gufan Yin, Yixin Bao +19
Early ranking stages in recommendation systems precompute item embeddings and cache them in-model for scoring within strict latency constraints. Because this cache exists only at s…
SlimPer: Make Personalization Model Slim and Smart
Siqi Wang, Xianjie Chen, Shaofeng Deng +42
Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely o…
Time-based Sequence Model for Personalization and Recommendation Systems
Tigran Ishkhanov, Maxim Naumov, Xianjie Chen +7
In this paper we develop a novel recommendation model that explicitly incorporates time information. The model relies on an embedding layer and TSL attention-like mechanism with in…
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi +21
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks…