{"id":"W3163325638","doi":"10.1109/tkde.2021.3075052","title":"Learning Hierarchical Review Graph Representations for Recommendation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada)","funders":"Youth Innovation Promotion Association; Youth Innovation Promotion Association of the Chinese Academy of Sciences; National Research Foundation Singapore; National Natural Science Foundation of China; Nanyang Technological University; National Research Foundation","keywords":"Computer science; Pooling; Graph; Recommender system; Artificial intelligence; Convolutional neural network; Recurrent neural network; Machine learning; Information retrieval; Theoretical computer science; Artificial neural network","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":[0.000664695,0.0008747086,0.0008660458,0.001806339,0.000298427,0.0007107144,0.001085681,0.0009535318,0.001994242],"category_scores_gemma":[0.004877248,0.0004130192,0.0009169254,0.002111702,0.0002813181,0.002039811,0.0005098816,0.001190876,0.001193557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008441636,"about_ca_system_score_gemma":0.0006838197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01365356,"about_ca_topic_score_gemma":0.03306761,"domain_scores_codex":[0.9994608,0.0001552187,0.00003279967,0.000202697,0.0001126692,0.00003590137],"domain_scores_gemma":[0.9985708,0.0005998067,0.0002172312,0.0002646816,0.0003002475,0.00004717723],"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":[0.0003686855,0.0002770057,0.007181252,0.0006361788,0.0004768585,0.0001928294,0.0002481356,0.2521788,0.01122319,0.01923596,0.02237755,0.6856034],"study_design_scores_gemma":[0.00001196157,0.0000656113,0.001381441,0.0000212465,0.00006854426,0.00006322197,0.00001955116,0.9811101,0.001214986,0.01265186,0.00337237,0.00001916445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06051541,0.004587523,0.9245968,0.0006269384,0.0001579169,0.0001905548,0.002251656,0.003022492,0.004050657],"genre_scores_gemma":[0.6900629,0.002904952,0.287755,0.0004989982,0.0002515714,0.0002916246,0.007545302,0.0002384624,0.01045114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01365356,"threshold_uncertainty_score":0.02714813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04649403019805645,"score_gpt":0.3242526013051148,"score_spread":0.2777585711070584,"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."}}