{"id":"W2798961129","doi":"10.48550/arxiv.1804.11335","title":"A Hybrid Recommendation Method Based on Feature for Offline Book Personalization","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Collaborative filtering; Personalization; Word2vec; Computer science; Recommender system; Preference; Similarity (geometry); Feature (linguistics); Order (exchange); Information retrieval; Artificial intelligence; Data mining; World Wide Web; Mathematics; Business; Statistics; 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":[0.0005946097,0.001014563,0.001703763,0.002506429,0.000676252,0.0008666761,0.001394153,0.001026696,0.003369998],"category_scores_gemma":[0.001729584,0.0004517264,0.001250611,0.002970335,0.0002230271,0.001799123,0.0005026567,0.0008021963,0.002833232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004145988,"about_ca_system_score_gemma":0.0006140425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01508044,"about_ca_topic_score_gemma":0.02800895,"domain_scores_codex":[0.9989046,0.0001161152,0.00007641851,0.0004181004,0.0003992947,0.0000854669],"domain_scores_gemma":[0.9988652,0.0002423783,0.00005381673,0.0002536181,0.0005318384,0.00005314492],"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":[0.0004371413,0.0003918993,0.008226301,0.0002181507,0.000382662,0.0002028293,0.0001419332,0.01389094,0.03041298,0.001345293,0.01584126,0.9285086],"study_design_scores_gemma":[0.0001142279,0.0004174454,0.01244656,0.00003135564,0.0003468141,0.001211401,0.00008587494,0.9490763,0.02261485,0.001655689,0.01182323,0.000176246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09042538,0.002520096,0.8904245,0.0003691731,0.0004474328,0.0002901785,0.001921092,0.0076976,0.00590446],"genre_scores_gemma":[0.4818397,0.0009233229,0.4944835,0.0002958218,0.0004073346,0.0002601872,0.002784081,0.000251295,0.01875465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01508044,"threshold_uncertainty_score":0.02998537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08301552119014027,"score_gpt":0.2378420668222554,"score_spread":0.1548265456321151,"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."}}