{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001976312,0.0005034119,0.0004874489,0.0003381849,0.0001869123,0.0001543603,0.00198274,0.0003330595,0.00006706181],"category_scores_gemma":[0.00002575823,0.0005841733,0.0004065869,0.00102681,0.0001312895,0.00085346,0.001565563,0.0008810904,0.0001146378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003503785,"about_ca_system_score_gemma":0.0001423928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001331684,"about_ca_topic_score_gemma":0.0005705245,"domain_scores_codex":[0.9970495,0.0002419419,0.0003389248,0.001731366,0.000113519,0.0005247404],"domain_scores_gemma":[0.9970381,0.0002333179,0.0004670759,0.001780782,0.0002497287,0.0002309983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001975764,0.0008666761,0.1118217,0.0002650721,0.0002566995,0.0002454123,0.0001728475,0.4491975,0.0002744689,0.4176203,0.003608262,0.01547342],"study_design_scores_gemma":[0.0008285112,0.00007479602,0.008028877,0.0001392063,0.00005888301,0.000001724657,0.00001436958,0.9497027,0.0001383001,0.03670383,0.003517646,0.0007911506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06214184,0.0001274927,0.9330086,0.0001362683,0.002298534,0.0006274786,0.00003251225,0.0003596678,0.001267621],"genre_scores_gemma":[0.995664,0.0002280855,0.002663604,0.0002867064,0.00006540446,0.000002979392,0.0001131994,0.00003084561,0.0009451985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9335222,"threshold_uncertainty_score":0.999661,"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."}}