{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003722527,0.0001287518,0.0001811187,0.00006162313,0.0004744876,0.0002444555,0.0008417192,0.0001214027,0.000213652],"category_scores_gemma":[0.003267889,0.0001109495,0.00005250194,0.0002729118,0.0003812593,0.0004635241,0.000555886,0.0002551833,0.00000760873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008532796,"about_ca_system_score_gemma":0.0004458867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001028137,"about_ca_topic_score_gemma":0.0005028367,"domain_scores_codex":[0.9985601,0.00003911931,0.0002331131,0.000283612,0.0006827626,0.0002012694],"domain_scores_gemma":[0.9981858,0.0001855415,0.0002342455,0.00007734961,0.001246892,0.00007015752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001165145,0.0001749894,0.005943261,0.00006401772,0.00007989592,0.000001603759,0.07264798,1.933689e-7,0.05585227,0.8334328,0.001656091,0.03003037],"study_design_scores_gemma":[0.002358108,0.0001875019,0.04306019,0.000921455,0.0001078622,0.000009595606,0.06553103,0.0008546506,0.1351033,0.6208698,0.1297992,0.001197287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9245878,0.00006519321,0.00005673836,0.0177752,0.001166581,0.0002571593,0.0001030116,0.00005675534,0.05593155],"genre_scores_gemma":[0.9981656,0.0005475031,0.0002985757,0.0002026539,0.000414635,0.00003100545,0.000008857069,0.000006701883,0.0003244873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.212563,"threshold_uncertainty_score":0.4524387,"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."}}