2020 · 58 citations · 27 references
ComputingBusiness AnalyticsComputer ScienceMachine LearningPredictive AnalyticsRecommender SystemsEvaluation Of Recommender SystemsFeature SelectionOnline RecommendationRecommendation EvaluationRecommendation SystemsConversion Rate ModelingCustomer Dwell TimeDwell Time EstimationFeature Engineering For Recommender SystemsFeature Selection For RecommendationFeature WeightingFeature-based RecommendationRanking Features
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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