{"id":"W2966283114","doi":"10.24963/ijcai.2019/537","title":"PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation","year":2019,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada)","funders":"Nanyang Technological University","keywords":"Discriminator; Computer science; Pairwise comparison; Kernel (algebra); Set (abstract data type); Personalization; Artificial intelligence; Generator (circuit theory); Component (thermodynamics); Determinantal point process; Process (computing); Point (geometry); Machine learning; Mathematics; World Wide Web","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":[0.001710722,0.001176443,0.001422135,0.0004677541,0.000314782,0.0006044524,0.001798055,0.001126076,0.002234218],"category_scores_gemma":[0.003963136,0.0005597136,0.0007122128,0.0006285992,0.0007097762,0.001102133,0.001061854,0.002165526,0.0009001276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007026641,"about_ca_system_score_gemma":0.0006137593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003091769,"about_ca_topic_score_gemma":0.005166986,"domain_scores_codex":[0.999243,0.0003281785,0.0000233752,0.00016162,0.0001727953,0.00007106414],"domain_scores_gemma":[0.9983855,0.001067042,0.00007105152,0.0002366354,0.000176197,0.0000635505],"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":[0.0001973758,0.0001287354,0.001501006,0.00009727589,0.0001225523,0.0001069163,0.00007031493,0.8687989,0.002446026,0.0163401,0.008824181,0.1013666],"study_design_scores_gemma":[0.000008558803,0.00002399428,0.00006326119,0.000004362646,0.000006146927,0.00002143079,0.000002823072,0.9952242,0.0002701499,0.003885307,0.0004857146,0.000003956457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01353823,0.0005818265,0.9819879,0.0003030987,0.00007213293,0.00006316079,0.0002012477,0.0008521269,0.002400299],"genre_scores_gemma":[0.7520493,0.0007726635,0.2336987,0.0009814989,0.0002288303,0.0003386773,0.001125784,0.000232099,0.01057259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003091769,"threshold_uncertainty_score":0.009047329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232480842433156,"score_gpt":0.2538897916954179,"score_spread":0.2306417074521023,"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."}}