CCN applies contrastive learning on collaborative co-click/co-non-click signals to structure item representations for trigger-induced recommendations, showing 12.3% CTR and 12.7% order lift in an unseen Taobao scenario after training on a year of heterogeneous data.
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Pith papers citing it
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cs.IR 2years
2024 2verdicts
UNVERDICTED 2representative citing papers
AMEN aligns item-scene interactions via homogeneous spaces and a TSP mechanism to let all-domain movelines differentially affect CTR predictions, reporting +11.6% CTCVR lift in A/B tests.
citing papers explorer
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Beyond the Trigger: Learning Collaborative Context for Generalizable Trigger-Induced Recommendation
CCN applies contrastive learning on collaborative co-click/co-non-click signals to structure item representations for trigger-induced recommendations, showing 12.3% CTR and 12.7% order lift in an unseen Taobao scenario after training on a year of heterogeneous data.
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All-domain Moveline Evolution Network for Click-Through Rate Prediction
AMEN aligns item-scene interactions via homogeneous spaces and a TSP mechanism to let all-domain movelines differentially affect CTR predictions, reporting +11.6% CTCVR lift in A/B tests.