{"id":"W4392927890","doi":"10.32920/25413835","title":"MCARS-CC: A Scalable Multi-context-aware Recommender System","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Recommender system; Scalability; Computer science; Cluster analysis; RSS; Data mining; Context (archaeology); Machine learning; Artificial intelligence; World Wide Web; Database","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":[0.001241602,0.001188762,0.001738507,0.001572125,0.001133356,0.001079691,0.002660299,0.001466766,0.002580271],"category_scores_gemma":[0.00386942,0.000589991,0.001064792,0.001905058,0.0002671273,0.002108621,0.001756492,0.001431367,0.002579266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006854489,"about_ca_system_score_gemma":0.001976174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0311588,"about_ca_topic_score_gemma":0.04378073,"domain_scores_codex":[0.9986433,0.0002725637,0.00009927955,0.0004250475,0.0004532872,0.0001064318],"domain_scores_gemma":[0.9979805,0.0003057856,0.0001101453,0.0007359249,0.0007101691,0.0001574782],"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":[0.0009561671,0.0004294346,0.005820904,0.0006746036,0.000692271,0.000600069,0.000264685,0.09326294,0.03324427,0.006548895,0.09604925,0.7614565],"study_design_scores_gemma":[0.0002029578,0.0002176624,0.00224602,0.00003242395,0.0001328619,0.0003157645,0.00009232469,0.9537286,0.01005447,0.003871366,0.02898259,0.0001229412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04216186,0.003792246,0.9005404,0.0007649914,0.0006764198,0.0006233611,0.00322921,0.04128645,0.006925152],"genre_scores_gemma":[0.3230128,0.001192575,0.6577884,0.000537792,0.0002623026,0.0003834985,0.006479698,0.000382932,0.009959966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0311588,"threshold_uncertainty_score":0.06195486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05118187470168477,"score_gpt":0.2888869477534357,"score_spread":0.2377050730517509,"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."}}