{"id":"W4385567939","doi":"10.1145/3580305.3599820","title":"Explicit Feature Interaction-aware Uplift Network for Online Marketing","year":2023,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Ranking (information retrieval); Constraint (computer-aided design); Key (lock); Component (thermodynamics); Data mining; Feature vector; Machine learning; Human–computer interaction; Artificial intelligence; Computer security; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056094,0.0001096676,0.0001409826,0.00007595713,0.0001381816,0.0001558529,0.0004578115,0.0000681331,0.00001489159],"category_scores_gemma":[0.0000319915,0.00008931538,0.00008653061,0.000435579,0.000003243361,0.0002738333,0.0002141891,0.0001087445,0.00002252549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002328109,"about_ca_system_score_gemma":0.00001534393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003118679,"about_ca_topic_score_gemma":0.00006297854,"domain_scores_codex":[0.9990979,0.00004521059,0.0001726585,0.0002968947,0.0001041893,0.0002830993],"domain_scores_gemma":[0.9990925,0.0003573342,0.00007134479,0.0003635252,0.00006332312,0.00005194656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005902021,0.00001574284,0.0006309369,0.00003347383,0.00001709037,0.000003455771,0.00007258979,0.00004651672,0.00003844703,0.01112619,0.9058833,0.08212631],"study_design_scores_gemma":[0.0001815779,0.00006235974,0.00300526,0.000139113,0.000003212399,0.00001788727,0.000174767,0.2664078,0.0003094584,0.002836555,0.7266113,0.0002507562],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001422526,0.00003939197,0.9841387,0.007833801,0.001271919,0.0003788854,0.000006706665,0.001812896,0.00309523],"genre_scores_gemma":[0.2843269,0.0001018335,0.6828295,0.002921996,0.002370498,0.0004670865,0.00013765,0.00006246254,0.02678208],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3013091,"threshold_uncertainty_score":0.3642176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03899027053013115,"score_gpt":0.3110624482009658,"score_spread":0.2720721776708347,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}