{"id":"W3185315936","doi":"10.2478/popets-2021-0059","title":"You May Also Like... Privacy: Recommendation Systems Meet PIR","year":2021,"lang":"en","type":"article","venue":"Proceedings on Privacy Enhancing Technologies","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Collusion; Protocol (science); Private information retrieval; Collaborative filtering; Consumption (sociology); Matrix decomposition; Theoretical computer science; Recommender system; Computer security; Information retrieval; Business","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":[0.009596041,0.0005098053,0.001071583,0.0006182003,0.002433662,0.005462393,0.001709261,0.003583105,0.01079766],"category_scores_gemma":[0.02929415,0.0008174268,0.0008778492,0.001037576,0.002713495,0.01082721,0.005357441,0.003900151,0.006980984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118016,"about_ca_system_score_gemma":0.002361521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001866302,"about_ca_topic_score_gemma":0.001054861,"domain_scores_codex":[0.987668,0.005057165,0.0006099864,0.001309171,0.004154418,0.001201217],"domain_scores_gemma":[0.9763618,0.008601485,0.001256321,0.01103992,0.002042815,0.0006978025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001556215,0.0005234597,0.007892136,0.0005852517,0.0002241746,0.0006035209,0.002058764,0.03553722,0.03317509,0.5466982,0.03106373,0.3400823],"study_design_scores_gemma":[0.0003674084,0.0009188105,0.002738356,0.0001894211,0.0001896903,0.001583155,0.001026438,0.3809916,0.06515269,0.4271068,0.1195476,0.0001879365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04588941,0.0006793805,0.9048142,0.009600296,0.0001794528,0.0004254038,0.000320092,0.004038034,0.03405359],"genre_scores_gemma":[0.780028,0.0006251996,0.2023132,0.002323466,0.0002352786,0.0003473017,0.0003456278,0.0003532777,0.01342854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01079766,"threshold_uncertainty_score":0.0507493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02120760173932964,"score_gpt":0.258623653431808,"score_spread":0.2374160516924784,"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."}}