{"id":"W2003528409","doi":"10.1109/is.2012.6335185","title":"Towards a new model for context-aware recommendation","year":2012,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Personalization; Recommender system; Information overload; Context (archaeology); Weighting; Consumption (sociology); Selection (genetic algorithm); Focus (optics); The Internet; Logistic regression; Order (exchange); Ranking (information retrieval); Machine learning; Information retrieval; Data science; World Wide Web","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":[0.003362647,0.0009756209,0.001798021,0.001647222,0.0009666824,0.002905602,0.003762465,0.002870578,0.00378875],"category_scores_gemma":[0.0106559,0.001045392,0.001782956,0.002771622,0.0009064556,0.00612476,0.001697821,0.003976498,0.002625288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001469599,"about_ca_system_score_gemma":0.001594266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01632825,"about_ca_topic_score_gemma":0.01737523,"domain_scores_codex":[0.997361,0.0009772547,0.0001893229,0.0006590135,0.0006586431,0.0001547211],"domain_scores_gemma":[0.9961481,0.002177396,0.0001872779,0.000471183,0.0008755194,0.0001405496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002282566,0.0002819096,0.003696989,0.0004106085,0.0003688088,0.0003114076,0.0008554932,0.4743458,0.003937452,0.3199657,0.0103493,0.1852483],"study_design_scores_gemma":[0.00002314599,0.00004201407,0.0001925114,0.00003158853,0.00004290479,0.00007071841,0.0000297692,0.9445706,0.0002760828,0.04998385,0.004709793,0.0000269182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002821529,0.0003418291,0.9938496,0.0005935896,0.00007946179,0.00006103188,0.0001653897,0.0003103122,0.001777278],"genre_scores_gemma":[0.1849036,0.001763567,0.8019387,0.0007508018,0.0004236909,0.0007937739,0.0008583466,0.000150635,0.008416813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01632825,"threshold_uncertainty_score":0.03246641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09596928842269453,"score_gpt":0.3194119588599157,"score_spread":0.2234426704372212,"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."}}