{"id":"W2560965260","doi":"10.1145/2988230","title":"Time-Aware Click Model","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Computer science; Perplexity; Relevance (law); Click-through rate; Information retrieval; Learning to rank; Search engine; Dwell time; Artificial intelligence; Language model","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":[0.001766309,0.001691184,0.001753118,0.002502467,0.0005306046,0.001785573,0.003375756,0.002879067,0.008585923],"category_scores_gemma":[0.00709397,0.0006687585,0.001778915,0.002740008,0.0009536171,0.004233839,0.0008802682,0.001625588,0.002759792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139318,"about_ca_system_score_gemma":0.001291148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01283372,"about_ca_topic_score_gemma":0.008402812,"domain_scores_codex":[0.9986324,0.000243179,0.00009289218,0.0003916193,0.0003694413,0.0002705838],"domain_scores_gemma":[0.9950461,0.002900261,0.0005554546,0.0004099092,0.0008175729,0.0002706562],"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":[0.00141735,0.0006790003,0.01642117,0.0004855375,0.0002457772,0.001051262,0.0004277377,0.785809,0.01118787,0.08258491,0.01033903,0.08935128],"study_design_scores_gemma":[0.00002791382,0.00007175939,0.001053778,0.000009836799,0.0000468567,0.0001521703,0.00001148473,0.9909893,0.0004226771,0.006270625,0.0009188732,0.00002483194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2406631,0.003167249,0.7262994,0.001832485,0.0004600713,0.0004013085,0.004859987,0.003854726,0.0184617],"genre_scores_gemma":[0.9481944,0.001185108,0.02640476,0.0002612678,0.0002496416,0.0002780845,0.001443767,0.0001618174,0.02182103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01283372,"threshold_uncertainty_score":0.02872282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02492444480584799,"score_gpt":0.2537497258937442,"score_spread":0.2288252810878962,"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."}}