{"id":"W4382282153","doi":"10.21203/rs.3.rs-3090614/v1","title":"Estimating serendipity in content-based recommender systems","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Serendipity; Recommender system; Relevance (law); Computer science; Surprise; Domain (mathematical analysis); Information retrieval; Collaborative filtering; World Wide Web; Measure (data warehouse); Data mining; Psychology; 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":[0.01170922,0.0013001,0.001975636,0.002799075,0.0008790784,0.002299825,0.001793614,0.002199758,0.0007326126],"category_scores_gemma":[0.06938791,0.001302064,0.0009913533,0.001889563,0.001292473,0.004257279,0.001745153,0.002076334,0.000484795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00164836,"about_ca_system_score_gemma":0.0007849722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009261863,"about_ca_topic_score_gemma":0.006557999,"domain_scores_codex":[0.9939679,0.003030645,0.0004308704,0.0009687195,0.001311599,0.0002903089],"domain_scores_gemma":[0.9309976,0.05670825,0.003523822,0.003493422,0.004580129,0.0006968355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009668894,0.0002943512,0.04262797,0.0004740581,0.0006987778,0.0004986753,0.0009113537,0.7855949,0.00506565,0.01648422,0.0019053,0.1444778],"study_design_scores_gemma":[0.00001236554,0.00009415345,0.002457778,0.00002962452,0.0000523412,0.000092901,0.0000442491,0.9899585,0.0009629146,0.005948948,0.0003163172,0.00002989616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1624848,0.002992548,0.8317841,0.0004902018,0.00007926983,0.0001455167,0.0001783809,0.000475638,0.001369547],"genre_scores_gemma":[0.8451635,0.001188372,0.1518443,0.0001167568,0.0001715584,0.00007887417,0.0003631439,0.00005542345,0.001018197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01170922,"threshold_uncertainty_score":0.06192505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3741743428969209,"score_gpt":0.446292249745643,"score_spread":0.07211790684872205,"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."}}