{"id":"W2020731303","doi":"10.2753/jec1086-4415170202","title":"Using Recommendation Agents to Cope with Information Overload","year":2012,"lang":"en","type":"article","venue":"International Journal of Electronic Commerce","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; Royal Bank of Canada","funders":"","keywords":"Information overload; Interactivity; Decision quality; Product (mathematics); Computer science; Quality (philosophy); Recommender system; Information quality; Marketing; Knowledge management; Information system; Business; 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":[0.006697617,0.0009790107,0.001021533,0.002405681,0.0009621369,0.003586062,0.001193939,0.001419926,0.002030764],"category_scores_gemma":[0.04235359,0.0006174887,0.0006839869,0.001451494,0.0006609021,0.00509489,0.001737392,0.001312269,0.0005635582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007070219,"about_ca_system_score_gemma":0.001222815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002530399,"about_ca_topic_score_gemma":0.002418826,"domain_scores_codex":[0.9951637,0.002472963,0.0004905075,0.0005005333,0.001190062,0.0001822353],"domain_scores_gemma":[0.9579568,0.03236948,0.003509288,0.003193001,0.002238277,0.0007332345],"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":[0.001923844,0.004525544,0.120919,0.001122973,0.0011123,0.0006750713,0.01100395,0.0511419,0.02266431,0.02341451,0.003332913,0.7581637],"study_design_scores_gemma":[0.001139864,0.00343577,0.07815925,0.0003764562,0.001335194,0.0009938269,0.003666286,0.7748977,0.02572153,0.0807279,0.02901987,0.0005263683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7230145,0.0005437807,0.2583032,0.0009961049,0.00009883659,0.0006178381,0.00009408801,0.00177448,0.01455714],"genre_scores_gemma":[0.8821453,0.0002340558,0.1156087,0.0001374527,0.00006915497,0.0002226326,0.0001012387,0.0000452297,0.00143618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006697617,"threshold_uncertainty_score":0.03542078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.290491872201308,"score_gpt":0.4871180956026505,"score_spread":0.1966262234013424,"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."}}