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most citedPersonalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users

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cs.IR2026

Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations

Zhuohang Jiang, Yuxin Chen, Shijie Wang +6

Cross-domain recommendation is a core problem in content-to-e-commerce platforms. Its objective is to leverage user interactions with content to infer potential purchasing intent o…

cs.IR20261 cited

Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users

Xiaodong Li, Jiawei Sheng, Jiangxia Cao +6

Cross-domain recommendation (CDR) has demonstrated to be an effective solution for alleviating the user cold-start issue. By leveraging rich user-item interactions available in a r…

cs.IR2026

KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation

Changle Qu, Sunhao Dai, Ke Guo +7

Live streaming platforms have become a dominant form of online content consumption, offering dynamically evolving content, real-time interactions, and highly engaging user experien…

cs.IR2026

SID-Coord: Coordinating Semantic IDs for ID-based Ranking in Short-Video Search

Guowen Li, Yuepeng Zhang, Shunyu Zhang +4

Large-scale short-video search ranking models are typically trained on sparse co-occurrence signals over hashed item identifiers (HIDs). While effective at memorizing frequent inte…

cs.IR2026

GRank: Towards Target-Aware and Streamlined Industrial Retrieval with a Generate-Rank Framework

Yijia Sun, Shanshan Huang, Zhiyuan Guan +4

Industrial-scale recommender systems rely on a cascade pipeline in which the retrieval stage must return a high-recall candidate set from billions of items under tight latency. Exi…

cs.IR2026

Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce

Zhiding Liu, Ben Chen, Mingyue Cheng +6

Search-based recommendation is one of the most critical application scenarios in e-commerce platforms. Users' complex search contexts--such as spatiotemporal factors, historical in…