{"id":"W3208809827","doi":"10.1145/3459637.3481910","title":"Dual Learning for Query Generation and Query Selection in Query Feeds Recommendation","year":2021,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Information retrieval; Selection (genetic algorithm); Generator (circuit theory); Web query classification; Readability; Filter (signal processing); Query expansion; Web search query; Sargable; Query optimization; Query language; Data mining; Search engine; Machine learning","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":[0.0004068754,0.00007259365,0.00008972514,0.0001010075,0.0001123533,0.0001575656,0.00004918817,0.00006485907,0.00001854659],"category_scores_gemma":[0.00009671176,0.00007903526,0.00002084478,0.0002105491,0.000004897001,0.0006018432,0.00006557517,0.0001144809,0.000002125619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007069978,"about_ca_system_score_gemma":0.00008494563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001538055,"about_ca_topic_score_gemma":0.0008607715,"domain_scores_codex":[0.9991598,0.00009737936,0.0001999325,0.000320264,0.00007621307,0.0001464156],"domain_scores_gemma":[0.9996811,0.00007783884,0.00004659461,0.00008003114,0.00008457914,0.00002990255],"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":[0.00001215324,0.00009424461,0.01780849,0.00006078837,0.00002152123,0.000006093678,0.001561351,0.02594131,0.08459815,0.08221891,0.001561048,0.7861159],"study_design_scores_gemma":[0.0002574212,0.00003109659,0.001769365,0.00000902905,0.00000211131,0.0000199813,0.00007212259,0.9839127,0.01020693,0.0007803725,0.002832163,0.0001066792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2128889,0.00003008866,0.7848285,0.001495694,0.0002103364,0.00008465078,1.438571e-7,0.00007347266,0.0003882606],"genre_scores_gemma":[0.8644523,0.00002744567,0.1339553,0.0003647347,0.0002223849,0.00002853572,0.00002319373,0.000006883785,0.000919164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9579714,"threshold_uncertainty_score":0.3222964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03996731213320634,"score_gpt":0.2708048305776304,"score_spread":0.230837518444424,"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."}}