{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006131552,0.00009962721,0.0001549026,0.00007606326,0.0002942284,0.0001308828,0.0004161846,0.00006115666,0.000169928],"category_scores_gemma":[0.00003152402,0.00009129046,0.00009563217,0.0001180152,0.000007063384,0.0005863402,0.0003936282,0.0001029973,0.00005220795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005449121,"about_ca_system_score_gemma":0.00002027381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001201028,"about_ca_topic_score_gemma":0.000005279555,"domain_scores_codex":[0.999108,0.00007609695,0.0001633084,0.0003196675,0.0001264138,0.0002065106],"domain_scores_gemma":[0.9994619,0.000126085,0.0001132168,0.0001815279,0.00007134589,0.00004594628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009809661,0.0001928345,0.0585412,0.0002839192,0.0002095839,0.000003793105,0.01628084,0.00006196236,0.005360191,0.4275613,0.02775193,0.4636543],"study_design_scores_gemma":[0.003416719,0.0007702612,0.00102876,0.00007934656,0.00001799007,0.00001889584,0.0008515121,0.3427511,0.007740615,0.006070017,0.6365452,0.0007095847],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008054736,0.00000706801,0.9664736,0.00182794,0.0007893828,0.0004924914,6.887379e-7,0.0004699596,0.02188409],"genre_scores_gemma":[0.942807,0.000004016869,0.05156038,0.0002517981,0.0001078363,0.00001738513,0.00001237769,0.000008598236,0.005230584],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9347523,"threshold_uncertainty_score":0.3722717,"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."}}