{"id":"W4399665456","doi":"10.18438/eblip30521","title":"Machine-learning Recommender Systems Can Inform Collection Development Decisions","year":2024,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Recommender system; Information retrieval; Collaborative filtering; Cosine similarity; World Wide Web; Naive Bayes classifier; Artificial intelligence; Support vector machine; Cluster analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.06326904,0.00220893,0.002811692,0.01270408,0.003255589,0.01400932,0.003286291,0.003464709,0.01958237],"category_scores_gemma":[0.2714585,0.001923578,0.00197048,0.01319642,0.001455906,0.0160467,0.004040523,0.004011099,0.01581813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003964604,"about_ca_system_score_gemma":0.006673637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01531493,"about_ca_topic_score_gemma":0.03431138,"domain_scores_codex":[0.9451729,0.03294729,0.005468388,0.006636658,0.008940301,0.0008344999],"domain_scores_gemma":[0.7784665,0.143187,0.01313103,0.02196724,0.04041819,0.002830174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001632342,0.0002818991,0.03478518,0.002728739,0.0007116499,0.0001852453,0.001904906,0.007684419,0.0006438664,0.02700471,0.186548,0.7373583],"study_design_scores_gemma":[0.0003174795,0.0004653058,0.03506631,0.0048822,0.001140303,0.0003579763,0.004621019,0.07388985,0.003014218,0.160311,0.7151734,0.000761042],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05540678,0.06753797,0.5703095,0.1175888,0.005252559,0.003731548,0.02355399,0.007607313,0.1490115],"genre_scores_gemma":[0.2747233,0.03070409,0.6469419,0.008711044,0.003134421,0.001717365,0.01760139,0.00129964,0.01516693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9859907,"threshold_uncertainty_score":0.3346027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02156616545465359,"score_gpt":0.2811858062530563,"score_spread":0.2596196407984028,"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."}}