{"id":"W3195874243","doi":"10.3390/bdcc5030035","title":"Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search","year":2021,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Search engine; Ranking (information retrieval); Metric (unit); Product (mathematics); Information retrieval; Quality (philosophy); Context (archaeology); Machine learning; Artificial intelligence; Artificial neural network; Data mining; Task (project management); Boosting (machine learning); Mathematics; 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":[0.001039025,0.000141923,0.0001975471,0.00005065705,0.0005640208,0.0004720949,0.0005268959,0.00002907422,0.000001151362],"category_scores_gemma":[0.0009996016,0.0001417884,0.00002393595,0.0002906439,0.000106859,0.0003484195,0.00167293,0.0001609378,0.000001019215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008177275,"about_ca_system_score_gemma":0.00005894428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009121321,"about_ca_topic_score_gemma":0.000007295372,"domain_scores_codex":[0.9982322,0.0001754995,0.0002633721,0.0008129736,0.0001672854,0.0003486477],"domain_scores_gemma":[0.9977229,0.001329723,0.0001155718,0.0005401736,0.000234837,0.00005679763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009980824,0.00002027067,0.008699242,0.00005667581,0.00001511126,0.000006239459,0.00009662657,0.0000257376,0.000183647,0.00174292,0.00009346972,0.9890501],"study_design_scores_gemma":[0.0008662064,0.00004000307,0.03769672,0.0001352674,0.00002382517,0.0000283924,0.0004572339,0.9555669,0.002909708,0.00115292,0.0008355115,0.0002873477],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1033576,0.001699602,0.8924614,0.001732546,0.0001948177,0.0002329668,0.00002571683,0.0001986752,0.00009662895],"genre_scores_gemma":[0.9588913,0.0000176946,0.04007022,0.0006033543,0.0002067253,0.000006372985,0.000184055,0.000008095451,0.00001219512],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9887627,"threshold_uncertainty_score":0.5781963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1775806205651802,"score_gpt":0.3566446157554741,"score_spread":0.1790639951902939,"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."}}