{"id":"W2533413719","doi":"10.1145/2983323.2983701","title":"Social Recommendation with Strong and Weak Ties","year":2016,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":165,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Natural Science Foundation of Zhejiang Province","keywords":"Interpersonal ties; Computer science; Recommender system; Pairwise comparison; Ranking (information retrieval); Crowdsourcing; Exploit; Artificial intelligence; Machine learning; Strong ties; Social network (sociolinguistics); Intuition; Bayesian probability; Bayesian network; Data science; Social media; World Wide Web; Psychology; Computer security; Social 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":[0.00329929,0.001063269,0.00198166,0.002261522,0.001369082,0.001765081,0.002355805,0.002681881,0.002651745],"category_scores_gemma":[0.01711952,0.0008666964,0.001435745,0.002635675,0.0007004914,0.003937714,0.001532239,0.001756106,0.001253841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001039868,"about_ca_system_score_gemma":0.0008263025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01256138,"about_ca_topic_score_gemma":0.02371122,"domain_scores_codex":[0.9968598,0.001103237,0.0002145673,0.0007827437,0.0008001567,0.0002395053],"domain_scores_gemma":[0.9914028,0.005587958,0.0007078154,0.001303839,0.000736835,0.0002607051],"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":[0.0007244043,0.00050516,0.03232299,0.0006415331,0.000814824,0.0005018807,0.0006078303,0.306607,0.003179139,0.04079896,0.01187002,0.6014262],"study_design_scores_gemma":[0.00005310728,0.0001196426,0.003662641,0.00005061552,0.0001159707,0.0002980189,0.00008147198,0.962101,0.00092507,0.02862435,0.003931096,0.000037064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1233668,0.00279333,0.8600202,0.001027083,0.0001183445,0.0002627877,0.0006638324,0.000950881,0.01079685],"genre_scores_gemma":[0.7732149,0.0008246915,0.2127958,0.0003919566,0.0003526168,0.000171322,0.0009920868,0.00008638066,0.01117021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01256138,"threshold_uncertainty_score":0.02497655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02608663920067936,"score_gpt":0.2594110181403761,"score_spread":0.2333243789396967,"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."}}