{"id":"W2792179143","doi":"10.1109/tkde.2018.2810873","title":"Supervised Search Result Diversification via Subtopic Attention","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China; Natural Science Foundation of Beijing Municipality; Microsoft Research","keywords":"Computer science; Pooling; Diversification (marketing strategy); Machine learning; Artificial intelligence; Ranking (information retrieval); Relevance (law); Information retrieval; Data mining","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":[0.0009022265,0.0007949315,0.001268835,0.00163139,0.0003794438,0.0006437277,0.001327967,0.0008360541,0.001711844],"category_scores_gemma":[0.003440482,0.0003080061,0.0007102059,0.001235085,0.0004952443,0.001735162,0.00131332,0.0007753071,0.0005762895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000629918,"about_ca_system_score_gemma":0.0008613532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002052244,"about_ca_topic_score_gemma":0.003858846,"domain_scores_codex":[0.9992861,0.0001491468,0.00004220844,0.0002005217,0.0002407203,0.00008129244],"domain_scores_gemma":[0.9986625,0.0005550958,0.0001747233,0.0002570915,0.0002524851,0.00009801517],"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":[0.0006484286,0.0006242339,0.006229951,0.0003181489,0.0002194816,0.0002729718,0.0003480582,0.1216967,0.04617574,0.01039646,0.01036512,0.8027046],"study_design_scores_gemma":[0.00006876501,0.0002381759,0.002297358,0.00001763729,0.00008831927,0.0002391108,0.00004875174,0.9710379,0.01023067,0.0133512,0.002356013,0.0000261131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2074952,0.002727224,0.7775038,0.0004092733,0.00008325421,0.0002414688,0.0002579835,0.003717197,0.007564608],"genre_scores_gemma":[0.8917843,0.0003882913,0.1020307,0.0002364096,0.0001207515,0.0001152123,0.0005596431,0.0001625557,0.004602088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002052244,"threshold_uncertainty_score":0.005726695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04992458604131879,"score_gpt":0.2878324295515918,"score_spread":0.237907843510273,"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."}}