{"id":"W2473235395","doi":"10.1145/2930238.2930293","title":"Effect of Different Implicit Social Networks on Recommending Research Papers","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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bookmarking; Recommender system; Computer science; Collaborative filtering; Audience measurement; Social network (sociolinguistics); World Wide Web; Information retrieval; Social media; Domain (mathematical analysis); 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":[0.001521164,0.0001182445,0.0002320893,0.0001366625,0.0001512363,0.00005380359,0.0006406688,0.00008391343,0.00004203575],"category_scores_gemma":[0.0000270779,0.00005840269,0.00009546415,0.0001900927,0.00003380366,0.0001033734,0.0002932349,0.0001371636,0.000008262355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008801867,"about_ca_system_score_gemma":0.000007010128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003568214,"about_ca_topic_score_gemma":0.000006309154,"domain_scores_codex":[0.9982932,0.0005508011,0.0002157423,0.0002897967,0.000292155,0.0003583358],"domain_scores_gemma":[0.9983519,0.001115177,0.00006251148,0.000372197,0.00003911294,0.00005914962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001972529,0.00003373525,0.00140663,0.00001865566,0.00002002704,0.000001610522,0.00007813184,1.591587e-7,0.005491456,0.1233927,0.01491877,0.8546184],"study_design_scores_gemma":[0.00505138,0.01743152,0.02590978,0.001500655,0.00002259547,0.00003236691,0.00008831559,0.007123863,0.8621101,0.01964711,0.05938314,0.001699121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1311183,0.00003905904,0.6029084,0.01203281,0.001237568,0.001207673,0.000002625384,0.0008358862,0.2506176],"genre_scores_gemma":[0.9989625,0.00001866678,0.0002773179,0.00006580473,0.000120247,0.00004845957,2.464923e-7,0.000008659968,0.0004981572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8678441,"threshold_uncertainty_score":0.2381592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04026202066835955,"score_gpt":0.3542433904589081,"score_spread":0.3139813697905485,"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."}}