{"id":"W2943217089","doi":"10.1145/3297280.3297382","title":"Study of linguistic features incorporated in a literary book recommender system","year":2019,"lang":"en","type":"article","venue":"","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Recommender system; Computer science; Pleasure; Reading (process); Key (lock); Natural language processing; Information retrieval; Quality (philosophy); Artificial intelligence; World Wide Web; Linguistics; Psychology","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":[0.0002393357,0.0001175206,0.0002921049,0.0003149683,0.0000156323,0.00003809174,0.0006160495,0.00004341836,0.00001240264],"category_scores_gemma":[0.00002542471,0.00009681723,0.00003644154,0.0007471019,0.000008446328,0.0002810446,0.0002595181,0.0001303809,0.00001691385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006848419,"about_ca_system_score_gemma":0.00002970456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000994698,"about_ca_topic_score_gemma":0.00005816495,"domain_scores_codex":[0.9988651,0.0001131954,0.0003536774,0.0003360173,0.0001904703,0.0001415536],"domain_scores_gemma":[0.9989054,0.00008667919,0.000161085,0.0007038146,0.0001108329,0.00003217756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001639171,0.006197253,0.4437258,0.0007441352,0.0005466499,0.0009268491,0.09177142,0.00103908,0.005433603,0.3879567,0.01148464,0.05000997],"study_design_scores_gemma":[0.01873416,0.01226331,0.2154319,0.004309484,0.0002701877,0.0003054041,0.01854852,0.4350972,0.0524759,0.2263438,0.009433059,0.006787116],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6156848,0.001265732,0.3326467,0.0001990087,0.0004561019,0.002465932,0.000001826504,0.001930513,0.04534943],"genre_scores_gemma":[0.9652565,0.000002939016,0.03355723,0.0001240139,0.000009943514,0.0000188178,0.000001221427,0.000007321183,0.001022015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4340581,"threshold_uncertainty_score":0.3948092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01113746719009497,"score_gpt":0.2691508151635969,"score_spread":0.2580133479735019,"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."}}