{"id":"W1866544678","doi":"10.1109/coec.2003.1210248","title":"PCFinder: an intelligent product recommendation agent for e-commerce","year":2003,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Collaborative filtering; Cluster analysis; Product (mathematics); Recommender system; Purchasing; E-commerce; Intelligent agent; Adaptation (eye); Order (exchange); Heuristic; Case-based reasoning; Web service; Data mining; World Wide Web; Artificial intelligence; Engineering","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":[0.0004401615,0.00009188274,0.00009906969,0.00005139186,0.000098951,0.0001055555,0.0003682601,0.00002662552,0.0000755369],"category_scores_gemma":[0.0001333226,0.00007334758,0.00004081203,0.0001202368,0.00001432658,0.0003505564,0.00004422646,0.00004256321,0.00004015076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002768425,"about_ca_system_score_gemma":0.00003580989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002226354,"about_ca_topic_score_gemma":0.00004168079,"domain_scores_codex":[0.999141,0.00006584533,0.0001677243,0.0003371357,0.00007877564,0.0002094777],"domain_scores_gemma":[0.9993478,0.00007552185,0.00004318646,0.0004264659,0.000062099,0.00004491736],"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":[0.00000472042,0.0001849782,0.0009274524,0.00001478809,0.00001400947,7.648763e-7,0.0006117409,0.00002549725,0.0002912562,0.4104612,0.01582392,0.5716396],"study_design_scores_gemma":[0.0005264326,0.000461758,0.003691016,0.0000105104,0.00001278995,0.00003228059,0.0006953753,0.02578452,0.113518,0.03781875,0.8169396,0.0005089397],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005402674,0.00004664239,0.9798303,0.004632176,0.0007281954,0.0003131591,4.553405e-7,0.0001938891,0.008852524],"genre_scores_gemma":[0.6361646,0.00003402723,0.3593376,0.002549458,0.00006452441,0.00006064349,0.0000102259,0.000009165086,0.001769724],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8011156,"threshold_uncertainty_score":0.2991028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07758792837610683,"score_gpt":0.3158030902829065,"score_spread":0.2382151619067997,"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."}}