{"id":"W4245466630","doi":"10.32920/ryerson.14644728","title":"Personalized recommender system on whom to follow in Twitter","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Recommender system; Computer science; Learning to rank; World Wide Web; Information retrieval; Social media; Graph; Rank (graph theory); Ranking (information retrieval); Theoretical computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008034728,0.0006994742,0.0009864579,0.001323007,0.001399039,0.001401529,0.001085736,0.001354211,0.007246366],"category_scores_gemma":[0.004251816,0.0003101288,0.0006473097,0.001580879,0.0001577882,0.002725033,0.0006973689,0.0009059708,0.006035456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005487867,"about_ca_system_score_gemma":0.0009287259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01750192,"about_ca_topic_score_gemma":0.04314615,"domain_scores_codex":[0.9992881,0.0001317124,0.00006568833,0.0002232356,0.0002078584,0.00008340888],"domain_scores_gemma":[0.9987313,0.0003931076,0.0001180053,0.0001967193,0.0004617399,0.00009916812],"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":[0.001695434,0.0007672398,0.09535266,0.001021889,0.0004322331,0.001628959,0.0016022,0.03561015,0.02734483,0.01136867,0.1311706,0.6920052],"study_design_scores_gemma":[0.0002070092,0.0004816296,0.04058962,0.0002342029,0.0007574796,0.001607097,0.002289042,0.8202268,0.01663557,0.01332485,0.1033857,0.0002610805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4265727,0.008296686,0.4358684,0.009108067,0.001881782,0.001539298,0.02314275,0.01029962,0.08329074],"genre_scores_gemma":[0.8112594,0.002613713,0.1325634,0.000681124,0.0005025206,0.0003444941,0.008936162,0.0001684752,0.04293067],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01750192,"threshold_uncertainty_score":0.03480011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06002510961448582,"score_gpt":0.2936494518470041,"score_spread":0.2336243422325183,"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."}}