{"id":"W2514497634","doi":"10.1609/icwsm.v10i1.14770","title":"Privacy Preference Inference via Collaborative Filtering","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Mitacs","keywords":"Homophily; Computer science; Collaborative filtering; Exploit; Inference; Dilemma; Graph; Personalization; Probabilistic logic; Set (abstract data type); Preference; Social graph; Latent variable; Neighbourhood (mathematics); Recommender system; Information privacy; Information retrieval; Internet privacy; Social media; Machine learning; Artificial intelligence; World Wide Web; Theoretical computer science; Computer security; Psychology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01070532,0.0009619655,0.002354595,0.003906864,0.001870329,0.002946011,0.00255213,0.002105855,0.002789317],"category_scores_gemma":[0.05233158,0.0009958654,0.002958728,0.0037815,0.001362224,0.004550898,0.002289441,0.00287397,0.0005117165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001941524,"about_ca_system_score_gemma":0.001873885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01212823,"about_ca_topic_score_gemma":0.01311132,"domain_scores_codex":[0.9873397,0.006633022,0.0006440129,0.002424934,0.00223165,0.0007266476],"domain_scores_gemma":[0.9514956,0.03742716,0.00225741,0.00610161,0.002169548,0.0005486664],"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":[0.0008253488,0.001020622,0.03687681,0.0004445111,0.0012969,0.0007075836,0.001473304,0.4997537,0.003577571,0.1621474,0.00723396,0.2846422],"study_design_scores_gemma":[0.00003016499,0.00003860171,0.001499714,0.0000225209,0.00006734601,0.00007964208,0.00009285254,0.9120315,0.0009317552,0.08435316,0.0008255118,0.0000271569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04352104,0.0001563723,0.9532401,0.0005678008,0.00002779658,0.00009424525,0.0003157066,0.0002672602,0.001809698],"genre_scores_gemma":[0.7816784,0.0002219763,0.2148203,0.0002205099,0.0001247875,0.0001934477,0.0009242286,0.00005158602,0.001764662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01212823,"threshold_uncertainty_score":0.05661583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05960817966836571,"score_gpt":0.3094195577504561,"score_spread":0.2498113780820904,"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."}}