{"id":"W2535417637","doi":"10.1145/2983323.2983827","title":"From Recommendation to Profile Inference (Rec2PI)","year":2016,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Recommender system; Inference; Context (archaeology); The Internet; Data science; Scope (computer science); Process (computing); Quality (philosophy); Data mining; World Wide Web; Artificial intelligence","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":[0.0001689602,0.00008205119,0.00009552976,0.00006511973,0.00003815587,0.00009143277,0.000495083,0.00003830981,0.0006399589],"category_scores_gemma":[0.00003717072,0.00004839191,0.00002426373,0.0001467512,0.000005153386,0.0005094729,0.0002334323,0.00002838176,0.0006765058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003838114,"about_ca_system_score_gemma":0.00002178371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003870815,"about_ca_topic_score_gemma":0.00004019714,"domain_scores_codex":[0.9992391,0.00004661855,0.0001716179,0.0002969443,0.0000929162,0.0001527297],"domain_scores_gemma":[0.9992864,0.0001227101,0.0000436863,0.0004095281,0.00005230464,0.00008539044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001074301,0.00001599306,0.0006736137,0.000001083501,0.000004522218,5.009274e-7,0.0001602413,2.470982e-8,0.004252965,0.02843073,0.06958857,0.8968707],"study_design_scores_gemma":[0.000400632,0.000248727,0.005567528,0.0001280803,0.000002243527,0.000003432268,0.00003593903,0.001483167,0.2044398,0.05002588,0.7371352,0.0005293854],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001648871,0.000002558152,0.9513346,0.01537077,0.0003392192,0.0001738171,0.000005884433,0.0004643352,0.03065994],"genre_scores_gemma":[0.7408218,0.000005666262,0.2555131,0.0009434122,0.0001077025,0.00008265486,0.000002918485,0.000005840366,0.002516874],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8963413,"threshold_uncertainty_score":0.869534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03029894752626068,"score_gpt":0.2935031062388504,"score_spread":0.2632041587125897,"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."}}