{"id":"W4241707254","doi":"10.32920/ryerson.14661834","title":"Recommender system on social networking site with domain specific and sparse data","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Recommender system; Collaborative filtering; Computer science; Information overload; Domain (mathematical analysis); Order (exchange); Scroll; World Wide Web; Information retrieval; Rank (graph theory); Scale-invariant feature transform; Artificial intelligence; Feature extraction; Engineering","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":[0.00235503,0.0007791481,0.002143443,0.001663897,0.001407111,0.001287004,0.002055523,0.001709433,0.002266773],"category_scores_gemma":[0.007506722,0.0005831445,0.001096215,0.002332345,0.000350212,0.002322942,0.001142022,0.001207352,0.001719978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007356009,"about_ca_system_score_gemma":0.00104025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03026789,"about_ca_topic_score_gemma":0.04257895,"domain_scores_codex":[0.9976963,0.0007094241,0.0001678547,0.0006561025,0.000584195,0.0001861267],"domain_scores_gemma":[0.9952648,0.002099331,0.0002805309,0.0009099463,0.001241418,0.0002041315],"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":[0.001160668,0.0007071085,0.02067903,0.0009655523,0.001144688,0.002211415,0.0007709321,0.4063516,0.02361764,0.02451215,0.04196452,0.4759147],"study_design_scores_gemma":[0.00006017323,0.0001191691,0.001691858,0.00001759166,0.00008802641,0.0002232039,0.00007162798,0.9888813,0.001504815,0.003533363,0.003778775,0.00003000703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09866475,0.00219583,0.8870515,0.001298438,0.0003620085,0.0004407621,0.001595669,0.003521436,0.004869613],"genre_scores_gemma":[0.518115,0.001513135,0.4647297,0.0003735483,0.0003624933,0.0003819775,0.002223996,0.00008996111,0.01221023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03026789,"threshold_uncertainty_score":0.06018341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1198283293113725,"score_gpt":0.2839963797789009,"score_spread":0.1641680504675284,"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."}}