arXiv:2606. 19108v1 Announce Type: new Abstract: Sequence modeling has become increasingly popular in recommendation and ranking algorithms, owing to its capacity to model users' historical behaviors and infer user intentions.
By Daochen Zha, Chun How Tan, Xin Liu, Bin Xu, Han Zhao, Xiaowei Liu, Tracy Yu, Hui Gao, Huiji Gao, Liwei He, Stephanie Moyerman, Sanjeev Katariya
arXiv:2606. 06779v1 Announce Type: cross Abstract: In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation.
By Nimesh Sinha, Raghav Saboo, Martin Wang, Sudeep Das
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck.
arXiv:2606. 11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior.
By Yifan Li, Jiahong Liu, Xinni Zhang, Hao Chen, Yankai Chen, Wenhao Yu, Jianting Chen, Irwin King
arXiv:2607. 12246v1 Announce Type: new Abstract: Personalization in two-sided marketplaces relies heavily on user-level features, yet for platforms with infrequent, high-consideration purchases, a large fraction of users lack sufficient history for effective recommendation, spanning both paid and organic channels.
By Wei Jiang, Bin Xu, Hui Gao, Bharathi Thangamani, Weiwei Guo, Sundar Srinivasavaradhan, Tracy Yu, Huiji Gao, Michael Kinoti
arXiv:2607. 22518v1 Announce Type: cross Abstract: In this paper, we propose a new solution for addressing the content cold-start problem in industry-scale search and recommender systems.
By Olafur Gudmundsson, Bo Zhao, Huayi Liao, Anna Kiyantseva, Sai Xiao, Heath Vinicombe, Mostafa Keikha, Luke DeLuccia, Zihao Chen, Junpeng Hou, Weijie Jiang, Bhawna Juneja, Andreanne Lemay, Wei-Ting Lin, Keyvan Moghadam, Jiaxing Qu, Zhiqing Rao, Zhihua Zhang