{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006481898,0.0003199398,0.0003389979,0.0003439122,0.0001925316,0.0001588646,0.0009939289,0.0002702268,0.00005221317],"category_scores_gemma":[0.00003823017,0.0003439859,0.0002639384,0.000238492,0.00002767349,0.0003293861,0.0004247481,0.0003164789,0.00001456022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000287294,"about_ca_system_score_gemma":0.0001582165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004653056,"about_ca_topic_score_gemma":0.000008982166,"domain_scores_codex":[0.9980611,0.000272552,0.0001937979,0.001121466,0.00008599958,0.0002650806],"domain_scores_gemma":[0.9980986,0.0001925945,0.0003817929,0.0009120534,0.0003105338,0.0001043898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004160126,0.0007196123,0.0006135301,0.001267399,0.0003931054,0.00009373175,0.0005742336,0.03062917,0.00009161294,0.2869227,0.6493292,0.02894969],"study_design_scores_gemma":[0.0004502157,0.0002012045,0.00003392654,0.0001572524,0.00003706929,0.000002918502,0.000007857433,0.8995394,0.0008482372,0.01631317,0.08206313,0.0003456074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002237411,0.00002171087,0.9929805,0.001698467,0.0007407998,0.0008418838,0.00006676124,0.0004351412,0.002990936],"genre_scores_gemma":[0.6529834,0.0001160632,0.3326951,0.004037934,0.0006995967,0.00003861191,0.0008840163,0.00008797764,0.008457372],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8689103,"threshold_uncertainty_score":0.9999012,"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."}}