{"id":"W2995930425","doi":"","title":"A Hybrid Recommendation Method Based on Feature for Offline Book Personalization","year":2019,"lang":"en","type":"article","venue":"Journal of Computers","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Collaborative filtering; Computer science; Word2vec; Personalization; Preference; Similarity (geometry); Recommender system; Feature (linguistics); Information retrieval; Order (exchange); Artificial intelligence; Data mining; World Wide Web; Statistics; Mathematics; Business; Embedding","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":[0.0004491106,0.0009593921,0.001530166,0.002580148,0.000610268,0.000772582,0.001166431,0.0008457982,0.003435479],"category_scores_gemma":[0.00112352,0.0004113545,0.00119141,0.002844444,0.0001889327,0.001591302,0.0004088941,0.0006106773,0.002586546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003911686,"about_ca_system_score_gemma":0.0005879682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01911062,"about_ca_topic_score_gemma":0.02985279,"domain_scores_codex":[0.9990394,0.0000782892,0.00007424235,0.000356688,0.0003678414,0.00008358691],"domain_scores_gemma":[0.9992774,0.0001236235,0.00003912241,0.0001279479,0.0003929147,0.00003907893],"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":[0.0004017063,0.0003294549,0.007996384,0.0001834856,0.000309354,0.0002049768,0.0001100975,0.01100205,0.02740722,0.0008736202,0.01484002,0.9363416],"study_design_scores_gemma":[0.0001313876,0.000425437,0.01873828,0.00003410685,0.0003627006,0.0015277,0.0001027125,0.9386308,0.02513309,0.001271173,0.01344374,0.0001988635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1056893,0.003031338,0.8707047,0.0003849033,0.0004711888,0.0003069958,0.002212956,0.01026313,0.006935496],"genre_scores_gemma":[0.5552816,0.001348239,0.4141982,0.0003320017,0.0004374101,0.0002710977,0.003553198,0.0002830614,0.02429518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01911062,"threshold_uncertainty_score":0.0379988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144623848386115,"score_gpt":0.2656781941869277,"score_spread":0.2512158093483162,"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."}}