The paper proposes an incremental recommendation system that uses causal modeling to avoid delivering redundant recommendations. By leveraging existing holdback data and a dual‑threshold targeting policy, the authors reduce recommendation impressions by 7% without harming overall content consumption. Joint training with holdback data also improves the calibration of the treated model, suggesting better generalisable representations than purely observational models.
By Athanasios Vlontzos, David Gustafsson, Michael O'Riordan, Ciar\'an M. Gilligan-Lee
The paper proposes an incremental recommendation approach that uses a causal model built from existing holdback data to avoid delivering redundant recommendations. By applying a dual‑threshold targeting policy, the system only recommends content when the likelihood of a treated stream is high and the likelihood of an organic stream is low, thereby reducing recommendation impressions by 7% without hurting overall consumption. Joint training with holdback data also improves the calibration of the treated head, suggesting that causal models capture more generalisable representations than purely observational models.
arXiv:2607. 01104v1 Announce Type: cross Abstract: In Large Language Model (LLM) training, data mixing plays a pivotal role in determining model performance.
By Zinan Tang, Yukun Zhang, Shaomian Zheng, Zhuoshi Pan, Qizhi Pei, Dingnan Jin, Jun Zhou, Yujun Wang, Biqing Huang
arXiv:2608. 10182v1 Announce Type: cross Abstract: Large-scale targeting and recommendation systems are typically built around predictive scores fed into heuristic or local allocation.
By Changshuai Wei, John Bencina, Phuc Nguyen, Andre Assuncao Silva T Ribeiro, Benjamin Zelditch
The paper introduces PUID, a Personalized Unobserved-Confounding-aware Interaction Deconfounder, designed to mitigate hidden confounding in recommender systems without relying on costly randomized controlled trials. PUID estimates user-item level sensitivity bounds using an entropy-based method that gauges the strength of hidden confounding from the mutual information between observed features and exposure status. An adversarial optimization strategy and a benchmark-guided variant (BPUID) further enhance robustness and predictive accuracy, and experiments on three real-world datasets show consistent outperformance over state-of-the-art baselines.
By Zongyu Li
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
The paper introduces DCEO, a data‑driven framework that learns item‑level proxy scores directly aligned with long‑term user objectives in e‑commerce search. It aggregates these scores into a user‑level metric, measures alignment via relative causal effect, and uses an actor‑critic model to generate context‑dependent fusion weights for multiple objectives. Offline experiments and a 41‑day online A/B test show DCEO improves GMV by 0.36% over traditional proxies.
By Junzhao Zhang, Tao Zhang, Liren Yu, Feiyi Dong, Zhixuan Zhang, Dan Ou, Haihong Tang
arXiv:2402.03941v4 Announce Type: replace-cross
Abstract: Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress...
By Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang
arXiv:2607. 20863v1 Announce Type: cross Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items.
By Wenyuan Wang, Yusong Zhao, Zihao Xu, Hengyi Wang, Qi Xu, Zhigang Hua, Yan Xie, Yi Wang, Zihao Zhao, Bo Long, Chengzhi Mao, Shuang Yang, Hengguan Huang, Hao Wang
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
By Jie Qiao, Ruichu Cai, Zijian Li, Weilin Chen, Pengfei Hua, Boyan Xu, Zhengming Chen, Zhifeng Hao, Peng Cui
DeGRe is a dense‑supervised generative reranking framework designed to improve multi‑stage recommender systems by addressing label bias and credit assignment issues. It uses an offline Lookahead Evaluator with beam search to generate dense supervision signals, which are distilled into a lightweight Online Generator that can perform efficient greedy decoding at inference time. Experiments show that DeGRe outperforms baselines on public benchmarks and industrial datasets, and it has been successfully deployed on Taobao Flash Shopping to enhance online recommendations.
By Chaotian Song, Jingyao Zhang, Chenghao Chen, Zisen Sang, Dehai Zhao, Guodong Cao, Boxi Wu, Deng Cai, Jia Jia
arXiv:2606. 09891v1 Announce Type: cross Abstract: Ranking in digital marketplaces is a dynamic exposure-allocation mechanism: displayed items shape discovery trajectories and success events logged by the platform to update future allocation policies.
By Ehsan Ebrahimzadeh, Sina Baharlouei, Abraham Bagherjeiran