Publication | Closed Access
Privileged Features Distillation at Taobao Recommendations
56
Citations
27
References
2020
Year
Unknown Venue
Artificial IntelligenceRanking AlgorithmEngineeringMachine LearningLearning To RankCvr PredictionText MiningInformation RetrievalData SciencePreference LearningManagementConversion RateE-commerce RecommendationsFeature EngineeringPredictive AnalyticsKnowledge DiscoveryComputer ScienceCold-start ProblemMarketingFeature ConstructionKnowledge DistillationInteractive MarketingPrivileged Features DistillationCollaborative Filtering
Features play an important role in the prediction tasks of e-commerce recommendations. To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available. However, the consistency in turn neglects some discriminative features. For example, when estimating the conversion rate (CVR), i.e., the probability that a user would purchase the item if she clicked it, features like dwell time on the item detailed page are informative. However, CVR prediction should be conducted for on-line ranking before the click happens. Thus we cannot get such post-event features during serving.
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