{"ID":5935718,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03362","arxiv_id":"2607.03362","title":"HGenPush: A Heterogeneous Generative Recommendation Architecture for Industrial Push Notification Systems","abstract":"With the explosive growth of content platforms, recommendation systems need to better satisfy user demands to enhance user satisfaction and retention. Taking short-video platforms as an example, users not only seek high-quality content but also trusted authors. Although generative recommendation systems have achieved breakthroughs in recent years, existing methods primarily generate single-type recommendation content and typically employ the inefficient autoregressive paradigm to generate semantic IDs. In this paper, we propose an end-to-end heterogeneous generative recommendation architecture called HGenPush. First, we design a hybrid user behavior understanding module that integrates multi-scenario and multi-perspective behaviors to capture precise user interest. Then, we design a dual-branch heterogeneous generative recommendation module that integrates video recommendation and author recommendation within a unified framework. In addition, to improve generation efficiency, we design a lightweight multi-token prediction method that discards the autoregressive paradigm. Finally, we design a user consumption preference alignment module, which leverages user feedback as reward signals to guide the model toward generating higher-quality content, thereby enhancing user experience and engagement. Through these designs, HGenPush simultaneously fulfills users' demands for high-quality content and trusted authors. We have deployed HGenPush on the push notification system of Kuaishou, a large-scale short-video platform, achieving a significant 0.181% increase in daily active users.","short_abstract":"With the explosive growth of content platforms, recommendation systems need to better satisfy user demands to enhance user satisfaction and retention. Taking short-video platforms as an example, users not only seek high-quality content but also trusted authors. Although generative recommendation systems have achieved b...","url_abs":"https://arxiv.org/abs/2607.03362","url_pdf":"https://arxiv.org/pdf/2607.03362v1","authors":"[\"Xiao Liang\",\"Jiali Feng\",\"Xin Feng\",\"Yiqing Wang\",\"Baolin Ye\",\"Siyao Feng\",\"Zhihui Deng\",\"Cunyi Zhang\",\"Huajin Sun\",\"Xuanping Li\",\"Kaiqiao Zhan\",\"Yanan Niu\",\"Kun Gai\"]","published":"2026-07-03T14:18:19Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[]","has_code":false}