{"id":"W411317080","doi":"","title":"SOCIAL INFLUENCE IN RECOMMENDATION AGENTS: CREATING SYNERGIES BETWEEN MULTIPLE RECOMMENDATION SOURCES FOR ONLINE PURCHASES","year":2012,"lang":"en","type":"article","venue":"Journal of the Association for Information Systems","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cognitive dissonance; Recommender system; Popularity; Computer science; Scope (computer science); Product (mathematics); The Internet; Social media; World Wide Web; Advice (programming); Internet privacy; Psychology","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":[0.004547409,0.0009592299,0.0008578553,0.00175777,0.001702303,0.002809225,0.001260232,0.001736352,0.004105921],"category_scores_gemma":[0.01709403,0.0009758808,0.0009634358,0.001218392,0.001201633,0.004501234,0.003156645,0.001363864,0.0009894095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007259955,"about_ca_system_score_gemma":0.00112249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003328345,"about_ca_topic_score_gemma":0.005539471,"domain_scores_codex":[0.9968615,0.001588564,0.0001711525,0.0005115125,0.0006945883,0.000172762],"domain_scores_gemma":[0.991776,0.005303447,0.000682093,0.00087407,0.0008666462,0.0004978548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002064769,0.002310831,0.04275964,0.0008970047,0.001596492,0.002488079,0.0131797,0.1592383,0.05055235,0.1350573,0.007111429,0.5827442],"study_design_scores_gemma":[0.0004993856,0.001224682,0.00747526,0.0001608397,0.0009421768,0.0007413647,0.001488738,0.8741809,0.01219392,0.07227241,0.02856595,0.0002542954],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3377873,0.001178439,0.6178752,0.002570293,0.0002184539,0.0006197692,0.0000982229,0.001311526,0.03834078],"genre_scores_gemma":[0.8499207,0.0002790665,0.1433373,0.0002005151,0.00008604527,0.000228109,0.00006535886,0.00005162684,0.005831244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004547409,"threshold_uncertainty_score":0.02404922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04858442794103821,"score_gpt":0.3425394312141456,"score_spread":0.2939550032731074,"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."}}