{"id":"W2950501737","doi":"10.48550/arxiv.1905.13132","title":"Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Information retrieval; Graph traversal; Cold start (automotive); Tree traversal; Weighting; Relevance (law); Recommender system; Similarity (geometry); Graph; Set (abstract data type); Data mining; Artificial intelligence; Algorithm; Theoretical computer science","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":[0.0007034537,0.001000194,0.001253331,0.004872885,0.001001721,0.001440286,0.002007152,0.001443696,0.00177058],"category_scores_gemma":[0.005368666,0.0007292889,0.001114203,0.005090215,0.0004628001,0.002983104,0.001000163,0.001186552,0.001297058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001514983,"about_ca_system_score_gemma":0.001225035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02481268,"about_ca_topic_score_gemma":0.05408785,"domain_scores_codex":[0.9988599,0.0002361911,0.00006992156,0.0004240179,0.000324232,0.00008579838],"domain_scores_gemma":[0.9968739,0.001341008,0.0004040944,0.0006106929,0.0006240773,0.0001462387],"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":[0.0008169931,0.0007049291,0.01373269,0.0006694556,0.000621142,0.0004058554,0.0006930203,0.3238709,0.0156096,0.02721542,0.02902886,0.5866312],"study_design_scores_gemma":[0.00005680751,0.0001002752,0.001805236,0.00003032,0.00008897107,0.0001232257,0.0001104666,0.9624492,0.004998074,0.02560902,0.004594665,0.00003369405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08516222,0.001174655,0.9032701,0.0003170044,0.00004964392,0.0002454368,0.003274524,0.003735955,0.002770453],"genre_scores_gemma":[0.4237583,0.0006144651,0.5539968,0.0001611857,0.00007243804,0.0002320318,0.0139193,0.000370693,0.006874772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02481268,"threshold_uncertainty_score":0.04933649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09263336830495084,"score_gpt":0.208855607958103,"score_spread":0.1162222396531521,"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."}}