{"id":"W4361759320","doi":"10.1007/978-3-031-28996-5_3","title":"Practical and Secure Federated Recommendation with Personalized Mask","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Computer science; Recommender system; Personalization; Cryptography; Matrix decomposition; Computer security; Information retrieval; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00103423,0.0005024922,0.0004665131,0.0007030909,0.0003565521,0.001102451,0.009883313,0.000443227,0.00002541713],"category_scores_gemma":[0.003768424,0.0004193006,0.00004430109,0.0009276184,0.001079805,0.001144664,0.03726865,0.001279146,0.00004099905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002436446,"about_ca_system_score_gemma":0.0006460614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001731815,"about_ca_topic_score_gemma":0.00009946171,"domain_scores_codex":[0.9962441,0.00005549263,0.0003750976,0.001878081,0.0008148666,0.0006323572],"domain_scores_gemma":[0.9949225,0.0009417931,0.0002878834,0.003497666,0.000212285,0.0001379286],"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":[0.00006001564,0.00006475988,0.0001556167,0.000148521,0.00008503506,0.0007225914,0.0007427173,0.0003236456,0.0001283838,0.05459795,0.01038791,0.9325829],"study_design_scores_gemma":[0.0003801962,0.0002204502,0.00005382458,0.0003202634,0.00001003387,0.0002749954,9.104793e-7,0.5597356,0.0003210805,0.4363124,0.001775289,0.0005949702],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004078506,0.00005397333,0.9370717,0.05994818,0.0006422367,0.0003670272,0.00001529166,0.0008661945,0.000994656],"genre_scores_gemma":[0.006645494,0.0001372464,0.9916813,0.001037667,0.0001255188,0.00001304314,0.00003508066,0.00004901921,0.0002756296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9319879,"threshold_uncertainty_score":0.9999345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04527433087333731,"score_gpt":0.2963128844159057,"score_spread":0.2510385535425684,"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."}}