{"id":"W3164333934","doi":"10.1145/3450289","title":"CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Recommender system; Collaborative filtering; Sampling (signal processing); Heuristic; Normalization (sociology); Machine learning; Offset (computer science); Sampling bias; Data mining; Convergence (economics); Stability (learning theory); Domain (mathematical analysis); Artificial intelligence; Sample size determination; Statistics","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":[0.004122044,0.001196465,0.002212005,0.001442834,0.0009636346,0.001251808,0.004388522,0.001993019,0.003421501],"category_scores_gemma":[0.01652946,0.0008916364,0.001281957,0.002015811,0.0009690386,0.002350342,0.0021584,0.002377072,0.001697291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00114883,"about_ca_system_score_gemma":0.00261491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01305302,"about_ca_topic_score_gemma":0.0248473,"domain_scores_codex":[0.9976325,0.0008055216,0.000121979,0.0005509727,0.0007230148,0.0001659569],"domain_scores_gemma":[0.993277,0.003857508,0.0002885204,0.001187246,0.001119079,0.0002705535],"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":[0.0007131508,0.0004471293,0.006159959,0.0003267944,0.0003005751,0.0001801285,0.0002866119,0.3685666,0.005587017,0.03297288,0.01593698,0.5685222],"study_design_scores_gemma":[0.00003471427,0.00003954348,0.0001309168,0.000008746536,0.00001590477,0.00004226853,0.000008741156,0.9929219,0.0007764092,0.004698463,0.001312002,0.00001038764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004317443,0.0003194151,0.9935036,0.0001017619,0.0000598007,0.0001081241,0.00009810292,0.0009562118,0.0005356072],"genre_scores_gemma":[0.222453,0.0004994828,0.7702453,0.0004643137,0.0002902548,0.0006262708,0.001012218,0.0002686568,0.004140606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01305302,"threshold_uncertainty_score":0.02595413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04093874193912365,"score_gpt":0.2890884400088174,"score_spread":0.2481496980696938,"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."}}