{"id":"W1600928385","doi":"10.1609/icwsm.v2i1.18624","title":"A Social Network Based Approach to Personalized Recommendation of Participatory Media Content","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International AAAI Conference on Web and Social Media","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Recommender system; Computer science; Citizen journalism; Bayesian network; Set (abstract data type); Social media; Preference; Diversity (politics); Personalization; World Wide Web; Social network (sociolinguistics); Content (measure theory); Information retrieval; Data science; Artificial intelligence; Sociology","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":[],"consensus_categories":[],"category_scores_codex":[0.0005073128,0.0001140178,0.00025174,0.00005870136,0.0001248668,0.00009279922,0.0005616063,0.00007578823,0.00002065856],"category_scores_gemma":[0.0002049356,0.00009099275,0.0001006662,0.0001792679,0.0000875188,0.0001421128,0.0002236438,0.0001268066,7.627507e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004440808,"about_ca_system_score_gemma":0.0001346572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009400822,"about_ca_topic_score_gemma":0.00001038517,"domain_scores_codex":[0.9988431,0.0000338061,0.0003216628,0.0002484141,0.0003932253,0.0001597924],"domain_scores_gemma":[0.9987902,0.0001292664,0.0002743519,0.00005241015,0.0007034844,0.00005029625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004936935,0.0001743462,0.002313685,0.00005225138,0.00006524179,2.061856e-7,0.01128852,4.812531e-7,0.006685126,0.9562742,0.006868729,0.01622784],"study_design_scores_gemma":[0.01358476,0.0008072426,0.1376449,0.002539526,0.0003470683,0.00004656476,0.04183974,0.1304214,0.2358522,0.310955,0.1224632,0.003498517],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8248278,0.0000830329,0.01889479,0.09791324,0.003085581,0.0009029357,0.0001133037,0.00021014,0.05396912],"genre_scores_gemma":[0.9943451,0.00001006171,0.004565525,0.0006918389,0.000266742,0.00005524885,0.000008325578,0.000006155846,0.00005095527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6453192,"threshold_uncertainty_score":0.3710577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1593367662843782,"score_gpt":0.3022923984066367,"score_spread":0.1429556321222586,"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."}}