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LiveForesighter: Generating Future Information for Live-Streaming Recommendations at Kuaishou
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Live-streaming, as a new-generation media to connect users and authors, has attracted a lot of attention and experienced rapid growth in recent years. Compared with the content-static short-video recommendation, the live-streaming recommendation faces more challenges in giving our users a satisfactory experience: (1) Live-streaming content is dynamically ever-changing along time. (2) valuable behaviors (e.g., send digital-gift, buy products) always require users to watch for a long-time (>10 min). Combining the two attributes, here raising a challenging question for live-streaming recommendation: How to discover the live-streamings that the content user is interested in at the current moment, and further a period in the future?
Forward citations
Cited by 2 Pith papers
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KuaiLive-M3: A Multi-Modal, Multi-Domain, and Multi-Feedback Dataset for Live Streaming Recommendation
KuaiLive-M3 releases multi-domain Kuaishou logs, ~88M segment multi-modal embeddings, and 25k questionnaires, with benchmarks showing gains from cross-domain transfer, temporal modeling, and sparse explicit feedback.
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Towards Generalizable Safety in Crowd Navigation via Conformal Uncertainty Handling
A crowd navigation method augmenting reinforcement learning with conformal uncertainty estimates is claimed to cut collisions under distribution shift, but the manuscript body is an unrelated live streaming dataset paper.
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