{"id":"W2964969589","doi":"10.48550/arxiv.1907.12375","title":"Personalized Attraction Enhanced Sponsored Search with Multi-task Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; Zhejiang University; National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Product (mathematics); Preference; Revenue; Point (geometry); Space (punctuation); World Wide Web; Recommender system; Click-through rate; Mobile device; Information retrieval; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003481569,0.001482787,0.003266647,0.001469688,0.0007004077,0.001407462,0.002326276,0.002603511,0.003271696],"category_scores_gemma":[0.007666494,0.0006124434,0.001000092,0.002035113,0.0008556092,0.002262451,0.001378154,0.002111775,0.001043848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001044277,"about_ca_system_score_gemma":0.001354427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008419201,"about_ca_topic_score_gemma":0.005833209,"domain_scores_codex":[0.998926,0.0004819398,0.00004992493,0.000222748,0.0001492682,0.0001702578],"domain_scores_gemma":[0.993593,0.004777057,0.0004688495,0.0003920105,0.0004157724,0.0003533722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001114821,0.001512175,0.004250998,0.0004076414,0.0002043251,0.0002509305,0.0001518613,0.7795946,0.001768982,0.004596133,0.007830316,0.1983171],"study_design_scores_gemma":[0.00006465465,0.00009164982,0.0001942923,0.000005180174,0.00001344055,0.0000170307,0.000007871096,0.997584,0.0001377498,0.001626948,0.0002500422,0.00000703822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3792638,0.009016224,0.5941758,0.002885747,0.0005522938,0.0003573779,0.0008666221,0.002690337,0.01019186],"genre_scores_gemma":[0.9302835,0.0006660261,0.06164594,0.0005001095,0.0004377631,0.0001440299,0.0006552687,0.00008789377,0.005579496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008419201,"threshold_uncertainty_score":0.01841253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09783682924353575,"score_gpt":0.2185551851905554,"score_spread":0.1207183559470197,"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."}}