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Click-Through Rate (CTR) prediction serves as a fundamental component inonline advertising. A common practice is to train a CTR model on advertisement(ad) impressions with user feedback. Since ad impressions are purposelyselected by the model itself, their distribution differs from the inferencedistribution and thus exhibits sample selection bias (SSB) that affects modelperformance. Existing studies on SSB mainly employ sample re-weightingtechniques which suffer from high variance and poor model calibration. Anotherline of work relies on costly uniform data that is inadequate to trainindustrial models. Thus mitigating SSB in industrial models with auniform-data-free framework is worth exploring. Fortunately, many platformsdisplay mixed results of organic items (i.e., recommendations) and sponsoreditems (i.e., ads) to users, where impressions of ads and recommendations areselected by different systems but share the same user decision rationales.Based on the above characteristics, we propose to leverage recommendationssamples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). Afterelaborating data augmentation, Rec4Ad learns disentangled representations withalignment and decorrelation modules for enhancement. When deployed in Taobaodisplay advertising system, Rec4Ad achieves substantial gains in key businessmetrics, with a lift of up to +6.6\% CTR and +2.9\% RPM.

Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao