{"id":"W3102793640","doi":"10.1145/3423322","title":"Neural Feature-aware Recommendation with Signed Hypergraph Convolutional Network","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Collaborative filtering; Recommender system; Bridging (networking); Hypergraph; Profiling (computer programming); Feature (linguistics); Convolutional neural network; Embedding; Graph; Information retrieval; Preference; Artificial intelligence; Machine learning; Data mining; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002343618,0.0002096285,0.0002336757,0.0001479289,0.0003675619,0.0004259353,0.0005976501,0.0001171224,0.00002160619],"category_scores_gemma":[0.000009074695,0.0001732373,0.00008669625,0.0007433589,0.00002227906,0.002893048,0.00001453933,0.0002543977,0.00009108942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007065004,"about_ca_system_score_gemma":0.00005826843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005707061,"about_ca_topic_score_gemma":0.00000530232,"domain_scores_codex":[0.9985664,0.0001219957,0.0004708968,0.0002263578,0.0003543349,0.0002600182],"domain_scores_gemma":[0.9987988,0.0001052978,0.0002656268,0.0004617143,0.0002115309,0.0001569834],"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":[0.0006641515,0.0003244743,0.001896739,0.001015933,0.0009342163,0.00001502478,0.01156785,0.1971079,0.0001270665,0.08062583,0.2242889,0.4814319],"study_design_scores_gemma":[0.002080605,0.001334805,0.0009625714,0.0002035788,0.00003784368,0.0002066495,0.001072361,0.6064156,0.0004345701,0.0002825875,0.3860054,0.0009634258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001306374,0.00002215457,0.9861092,0.01016528,0.000756245,0.0005818291,0.00003176125,0.0007204001,0.001482491],"genre_scores_gemma":[0.984769,0.00001021752,0.01275742,0.00197892,0.0001314769,0.0002113616,0.00007828934,0.00001097834,0.00005229191],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9846384,"threshold_uncertainty_score":0.7064412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02140057928735824,"score_gpt":0.221194823307047,"score_spread":0.1997942440196888,"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."}}