output
20162025
most citedNeural Rating Regression with Abstractive Tips Generation for Recommendation

306 citations

Showing 2023Show all

11 papers · 1 filter

cs.LG2023116 cited

Federated Continual Learning via Knowledge Fusion: A Survey

Xin Yang, Hao Yu, Xin Gao +3

Data privacy and silos are nontrivial and greatly challenging in many real-world applications. Federated learning is a decentralized approach to training models across multiple loc…

cs.IR20233 cited

Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models

Jinbo Song, Ruoran Huang, Xinyang Wang +9

Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, in…

cs.CV20234 cited

Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysis

Peipei Li, Xing Cui, Yibo Hu +3

Point cloud analysis faces computational system overhead, limiting its application on mobile or edge devices. Directly employing small models may result in a significant drop in pe…

cs.IR20236 cited

Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification

Chenyu Zhao, Yunjiang Jiang, Yiming Qiu +2

Retrieval augmentation, which enhances downstream models by a knowledge retriever and an external corpus instead of by merely increasing the number of model parameters, has been su…

cs.CV20234 cited

Learning and Evaluating Human Preferences for Conversational Head Generation

Mohan Zhou, Yalong Bai, Wei Zhang +3

A reliable and comprehensive evaluation metric that aligns with manual preference assessments is crucial for conversational head video synthesis methods development. Existing quant…

cs.CV202331 cited

LGViT: Dynamic Early Exiting for Accelerating Vision Transformer

Guanyu Xu, Jiawei Hao, Li Shen +4

Recently, the efficient deployment and acceleration of powerful vision transformers (ViTs) on resource-limited edge devices for providing multimedia services have become attractive…