{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005968609,0.001505039,0.00207805,0.002781893,0.0008482549,0.001696771,0.002893567,0.002128638,0.00233951],"category_scores_gemma":[0.01653729,0.000797178,0.001326901,0.002313945,0.001182162,0.003827235,0.002278889,0.002246018,0.001768666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200207,"about_ca_system_score_gemma":0.001612124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005782856,"about_ca_topic_score_gemma":0.00718598,"domain_scores_codex":[0.9955343,0.001967785,0.0003305049,0.001036269,0.0008294144,0.0003017063],"domain_scores_gemma":[0.9890175,0.007428281,0.0005636748,0.001216922,0.001421194,0.0003524857],"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":[0.001621905,0.001563513,0.009774832,0.0005039793,0.0002130152,0.0003017191,0.0005015225,0.1471528,0.02487491,0.00808779,0.01179599,0.793608],"study_design_scores_gemma":[0.00006561578,0.0001553852,0.0005281971,0.000007833638,0.00003005576,0.00008714491,0.00003366773,0.9895705,0.00473044,0.003564961,0.001203508,0.00002271393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03586191,0.0008277236,0.9578473,0.000529486,0.00006537034,0.0003184874,0.0003559219,0.002989475,0.001204267],"genre_scores_gemma":[0.5406891,0.0005533451,0.4481075,0.0006038435,0.0003314507,0.0007493109,0.002766293,0.0003294791,0.005869695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005968609,"threshold_uncertainty_score":0.03156543,"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."}}