{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002962213,0.0001006747,0.0001503309,0.00009661326,0.0001584115,0.00008299175,0.0002633084,0.00003913018,0.00001172021],"category_scores_gemma":[0.00002486308,0.0001032834,0.00004824166,0.0002980326,0.000006442916,0.0004742716,0.00001374136,0.0001850286,0.000004344913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001442548,"about_ca_system_score_gemma":0.00003065887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004319665,"about_ca_topic_score_gemma":0.000009611988,"domain_scores_codex":[0.9991678,0.00004861224,0.000209676,0.0003858417,0.00005334167,0.0001347323],"domain_scores_gemma":[0.9990819,0.0002299335,0.00002884694,0.0005277234,0.00006624627,0.00006531111],"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":[0.000003466255,0.0001846628,0.000009909862,0.001242023,0.0001019502,0.000004998812,0.0003333116,0.0007638694,0.001129815,0.004844619,0.01182279,0.9795586],"study_design_scores_gemma":[0.0005529416,0.0001584852,0.00005628011,0.001364047,0.00007423177,0.0001227792,0.00003794824,0.5360892,0.0122725,0.0004730817,0.4483151,0.0004834521],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002414277,0.001856027,0.9957923,0.001103429,0.0004161988,0.0001884099,0.00003521095,0.0002313289,0.0003530069],"genre_scores_gemma":[0.4166996,0.04266735,0.5361134,0.0007374038,0.000368614,0.0008602386,0.0007808705,0.00009759684,0.001674979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9790751,"threshold_uncertainty_score":0.4211774,"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."}}