{"id":"W3055273286","doi":"10.1109/tvcg.2020.3030387","title":"VizCommender: Computing Text-Based Similarity in Visualization Repositories for Content-Based Recommendations","year":2020,"lang":"en","type":"preprint","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Visualization; Relevance (law); Information retrieval; Similarity (geometry); Visual analytics; Latent Dirichlet allocation; Similarity measure; Analytics; Tag cloud; Topic model; Data science; Measure (data warehouse); Recommender system; Information visualization; World Wide Web; Data mining; Artificial intelligence; Image (mathematics)","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":[0.002018348,0.001514646,0.001312401,0.01145473,0.001092995,0.003636641,0.002146634,0.00219871,0.00687265],"category_scores_gemma":[0.01753506,0.0005226122,0.001195457,0.009657452,0.0004984116,0.005547402,0.002788934,0.001085822,0.005055461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001340968,"about_ca_system_score_gemma":0.001233874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01153179,"about_ca_topic_score_gemma":0.01722413,"domain_scores_codex":[0.9974572,0.0004725863,0.0002782113,0.0005838305,0.001038317,0.0001698064],"domain_scores_gemma":[0.9951392,0.001522384,0.0004960524,0.001406966,0.001130035,0.0003052347],"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":[0.001323232,0.0008532859,0.02484419,0.00110424,0.0006122339,0.0004874007,0.00135052,0.03803891,0.01941872,0.008565994,0.04813588,0.8552653],"study_design_scores_gemma":[0.0001963708,0.0006030162,0.01557045,0.0001104512,0.0001305431,0.0004906561,0.0008559288,0.9194844,0.01850568,0.01988773,0.02403224,0.0001325116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2131148,0.004094059,0.6515427,0.001163049,0.0004532512,0.001477065,0.02395106,0.09425947,0.009944507],"genre_scores_gemma":[0.4323157,0.0006996533,0.5304053,0.0001652194,0.000153698,0.0006605309,0.02874942,0.001279167,0.005571269],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01153179,"threshold_uncertainty_score":0.0229913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07907896541989255,"score_gpt":0.3382526641997582,"score_spread":0.2591736987798656,"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."}}