{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005110401,0.0001689792,0.0002229652,0.0002293314,0.000385225,0.0005421978,0.0004092858,0.00009110953,0.00002332225],"category_scores_gemma":[0.00003710855,0.0001615925,0.00008165307,0.0006265857,0.00001429754,0.002247667,0.00001226126,0.0001185576,0.00004794802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001724449,"about_ca_system_score_gemma":0.0001720733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008447406,"about_ca_topic_score_gemma":0.00002810917,"domain_scores_codex":[0.9985135,0.0001788662,0.00060104,0.0002436113,0.0002493631,0.000213593],"domain_scores_gemma":[0.9977778,0.0002347736,0.0002649768,0.0007162896,0.0009051422,0.0001009843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001677208,0.0007707682,0.00002959537,0.000322091,0.0003635844,0.000002598898,0.02186433,0.05597633,0.0002102205,0.2876828,0.01861562,0.6139944],"study_design_scores_gemma":[0.001569094,0.0006847052,0.0001198491,0.0001430343,0.00002492175,0.00006698832,0.008072432,0.3712861,0.01021792,0.0009855392,0.6062213,0.0006081838],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001794617,0.00001849411,0.9931695,0.001081838,0.001469196,0.0009641193,0.0002065774,0.0003505583,0.00256025],"genre_scores_gemma":[0.8692347,0.00002176389,0.12849,0.0005406593,0.00007006241,0.001201527,0.000230075,0.00001409581,0.000197123],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8690553,"threshold_uncertainty_score":0.6589552,"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."}}