{"id":"W3119545471","doi":"10.1609/aaai.v35i5.16553","title":"Knowledge-Enhanced Top-K Recommendation in Poincaré Ball","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Interpretability; Computer science; Recommender system; Knowledge graph; Learning to rank; Graph; Regularization (linguistics); Artificial intelligence; Machine learning; Information retrieval; Theoretical computer science; Ranking (information retrieval)","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":[],"consensus_categories":[],"category_scores_codex":[0.0006255289,0.0001801197,0.0002774284,0.0001489807,0.0001013099,0.000248963,0.001268108,0.0001022269,0.00008918812],"category_scores_gemma":[0.0002591995,0.0001517888,0.0001039764,0.000958393,0.00006700282,0.000481209,0.0004731304,0.0002864768,0.00005533853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008282522,"about_ca_system_score_gemma":0.0001449486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000519513,"about_ca_topic_score_gemma":0.00009156544,"domain_scores_codex":[0.9982891,0.00004433711,0.0006223839,0.0005214523,0.0002274632,0.00029527],"domain_scores_gemma":[0.998592,0.00008885706,0.0002850446,0.0003132017,0.0006597528,0.0000611861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001097992,0.0001530672,0.0002253881,0.00003929788,0.000007399224,6.006653e-7,0.001532075,0.000002156709,0.04830756,0.6795357,0.0003555581,0.2698303],"study_design_scores_gemma":[0.00002817562,0.00007726769,0.0003024256,0.0002682084,0.000002834877,0.000005471812,0.0004572408,0.01167514,0.8547197,0.1315131,0.0007727215,0.000177729],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2018478,0.000162068,0.3694282,0.0300783,0.002858925,0.00135246,0.000008491949,0.0005198134,0.393744],"genre_scores_gemma":[0.9941267,0.00006880577,0.005127198,0.0001966845,0.00005169022,0.00004182159,8.396613e-7,0.000009306302,0.0003769953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8064122,"threshold_uncertainty_score":0.618977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08167864707999131,"score_gpt":0.3210844721252952,"score_spread":0.2394058250453039,"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."}}