{"id":"W4392368241","doi":"10.1145/3616855.3635859","title":"MultiFS: Automated Multi-Scenario Feature Selection in Deep Recommender Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Hong Kong Institute for Data Science; National Natural Science Foundation of China; Innovation and Technology Fund; Universitas Brawijaya; Impact Fund; City University of Hong Kong; Aromatic Plant Research Center","keywords":"Computer science; Recommender system; Feature (linguistics); Bridge (graph theory); Feature selection; Artificial intelligence; Machine learning; Selection (genetic algorithm); Data mining","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":[0.001678996,0.001390124,0.001280946,0.001202995,0.0005986905,0.000750265,0.001849329,0.001270547,0.002272099],"category_scores_gemma":[0.003874828,0.0005947786,0.001020491,0.001191691,0.0003623558,0.002001367,0.001303696,0.001584183,0.0008000294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007313252,"about_ca_system_score_gemma":0.00119772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01085014,"about_ca_topic_score_gemma":0.02232346,"domain_scores_codex":[0.9989818,0.0003515974,0.00007963732,0.0002853813,0.0001755084,0.0001260591],"domain_scores_gemma":[0.9986461,0.0007068698,0.00008748398,0.0002224341,0.0002661527,0.00007098688],"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":[0.0005182779,0.0004587187,0.01229086,0.0002386748,0.000399744,0.0002399028,0.0001858264,0.4282774,0.00795626,0.004414548,0.01629954,0.5287203],"study_design_scores_gemma":[0.00002513621,0.00006748721,0.0007562204,0.000006847853,0.000016776,0.00003227267,0.00002046071,0.9944997,0.000925811,0.002724464,0.0009124226,0.00001244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07635192,0.001142249,0.9112526,0.0006196758,0.0001107501,0.0002130416,0.001751432,0.006709774,0.001848566],"genre_scores_gemma":[0.70011,0.0002762157,0.2926267,0.0003989816,0.0001061314,0.0002176851,0.003958027,0.0001617149,0.00214461],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01085014,"threshold_uncertainty_score":0.02157396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02368038919726357,"score_gpt":0.2903076922954795,"score_spread":0.266627303098216,"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."}}