{"id":"W4385060564","doi":"10.1007/978-3-031-38333-5_29","title":"RKTUP Framework: Enhancing Recommender Systems with Compositional Relations in Knowledge Graphs","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"MovieLens; Computer science; Recommender system; Knowledge graph; Relation (database); Embedding; Graph; Task (project management); Translation (biology); Artificial intelligence; Information retrieval; Machine learning; Theoretical computer science; Collaborative filtering; Data mining; Engineering","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":[0.001646022,0.0008373553,0.001081772,0.001336044,0.0006194486,0.001988993,0.002446718,0.000960772,0.006987324],"category_scores_gemma":[0.007345573,0.0005896841,0.001100517,0.001910432,0.0004919497,0.004269623,0.003172977,0.001801578,0.002877449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006817598,"about_ca_system_score_gemma":0.001200413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006495662,"about_ca_topic_score_gemma":0.01413916,"domain_scores_codex":[0.9986653,0.0005182942,0.00006411163,0.0002440292,0.0004414296,0.00006675869],"domain_scores_gemma":[0.9974509,0.001132779,0.00009183304,0.0007817288,0.0004270224,0.0001158335],"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":[0.000356244,0.0004443395,0.001419184,0.0009008553,0.0003518449,0.0002714215,0.0004733203,0.1339671,0.009589514,0.13643,0.03496823,0.6808277],"study_design_scores_gemma":[0.00003828284,0.00006318266,0.0002275573,0.00004416682,0.00009735033,0.00008798494,0.00006581038,0.9034305,0.003778858,0.07334943,0.01879202,0.00002481883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004758089,0.0003821546,0.9878359,0.0002108301,0.00006509188,0.00009213131,0.0004957262,0.003171345,0.002988564],"genre_scores_gemma":[0.1040901,0.0006482855,0.8872044,0.0001648329,0.00006823853,0.0001775157,0.001756721,0.0004145018,0.005475421],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006987324,"threshold_uncertainty_score":0.02337497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02386635591196934,"score_gpt":0.250923987171444,"score_spread":0.2270576312594746,"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."}}