1 citations · 1 across the 7 of their papers we have counts for
7 papers
DEGR: Dual Exploration-Driven Generative Re-Ranking for Adaptive Cross-Request Context Bridging
Binglei Zhao, Xuanhua Yang, Xiwei Zhao +1
In industrial recommendation systems, the re-ranking stage balances business objectives and diversity for sequence-level optimization while modeling contextual information. However…
GenRec: A Preference-Oriented Generative Framework for Large-Scale Recommendation
Yanyan Zou, Junbo Qi, Lunsong Huang +7
Generative Retrieval (GR) offers a promising paradigm for recommendation through next-token prediction (NTP). However, scaling it to large-scale industrial systems introduces three…
Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning
Xuanhua Yang, Jianxin Zhao, Shaoguo Liu +2
Multi-task learning (MTL) has been widely applied in online advertising and recommender systems. To address the negative transfer issue, recent studies have proposed optimization m…
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
Zihan Lin, Xuanhua Yang, Xiaoyu Peng +4
In modern advertising and recommender systems, multi-task learning (MTL) paradigm has been widely employed to jointly predict diverse user feedbacks (e.g. click and purchase). Whil…
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
Xuanhua Yang, Xiaoyu Peng, Penghui Wei +3
Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple…
Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding
Penghui Wei, Shaoguo Liu, Xuanhua Yang +2
Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle cr…
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
Penghui Wei, Xuanhua Yang, Shaoguo Liu +2
This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). W…