{"id":"W2026962484","doi":"10.1007/s10115-006-0002-1","title":"A collaborative filtering framework based on fuzzy association rules and multiple-level similarity","year":2006,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Council Canada; Hong Kong Polytechnic University; University of Rochester","keywords":"Collaborative filtering; Recommender system; Computer science; Association rule learning; Similarity (geometry); Popularity; Fuzzy logic; Data mining; Product (mathematics); Information retrieval; Quality (philosophy); The Internet; Machine learning; Artificial intelligence; World Wide Web; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005764916,0.0008993491,0.003073223,0.004016074,0.001923085,0.003711778,0.004292514,0.003196993,0.002752561],"category_scores_gemma":[0.01106684,0.0008810489,0.00248067,0.004648945,0.001254803,0.00596575,0.002096414,0.0021061,0.001280918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001095916,"about_ca_system_score_gemma":0.001777428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01105787,"about_ca_topic_score_gemma":0.01103358,"domain_scores_codex":[0.9943494,0.00163193,0.0004199911,0.001158028,0.002213355,0.0002273281],"domain_scores_gemma":[0.9936243,0.003177907,0.0003299385,0.0008814778,0.001763565,0.0002228238],"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":[0.0003013008,0.0005448804,0.002800656,0.0005228191,0.001022064,0.0004599663,0.000981222,0.2260992,0.006553807,0.2760389,0.006257436,0.4784178],"study_design_scores_gemma":[0.00004196502,0.0001159643,0.0005234499,0.00004495908,0.0002050982,0.0003105053,0.00005683815,0.9217408,0.001562546,0.06991953,0.005394578,0.00008384184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002150224,0.0003092919,0.9964676,0.0001056407,0.00003941719,0.00003515701,0.00003302104,0.0001287119,0.0007309041],"genre_scores_gemma":[0.1225132,0.0006923485,0.8723636,0.0001445071,0.000213196,0.000142239,0.0002262991,0.00004633029,0.003658395],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01105787,"threshold_uncertainty_score":0.03048813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01406702293136997,"score_gpt":0.2411199238886861,"score_spread":0.2270529009573161,"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."}}