{"id":"W4404344839","doi":"10.48550/arxiv.2411.00341","title":"A Survey on Bundle Recommendation: Methods, Applications, and Challenges","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Bundle; Computer science; Bundle adjustment; Data science; Artificial intelligence; Materials science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001224234,0.0002620849,0.000309676,0.0002836214,0.0001115259,0.0002103643,0.0008966447,0.0002466071,0.00001082432],"category_scores_gemma":[0.00001705985,0.0002771772,0.00009247753,0.0003310664,0.00003945705,0.0001265732,0.002228049,0.0005124676,0.00005252362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001167034,"about_ca_system_score_gemma":0.00007503723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003277828,"about_ca_topic_score_gemma":0.0001308153,"domain_scores_codex":[0.9978147,0.0005600867,0.0001964326,0.001186845,0.0000488551,0.0001931209],"domain_scores_gemma":[0.9982718,0.0003494346,0.0001608913,0.001013163,0.00009280492,0.000111928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007308959,0.0001305546,0.0002390841,0.0003707733,0.0001378485,0.00001733982,0.0002141476,0.0001269487,0.000002963365,0.7958089,0.003393058,0.1995511],"study_design_scores_gemma":[0.0003293109,0.000263598,0.003159517,0.000387645,0.00007497591,0.00001438628,0.0001194042,0.1286774,0.0002894858,0.6737872,0.1916895,0.001207506],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006534699,0.00250403,0.9835822,0.001989422,0.0005586466,0.0005909334,0.00003843965,0.0006515654,0.009431272],"genre_scores_gemma":[0.9496204,0.01377437,0.0346462,0.0001894084,0.0001544402,0.00002988956,0.00006634348,0.00003976655,0.001479189],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9489669,"threshold_uncertainty_score":0.9999681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2373370112375228,"score_gpt":0.2720765801573096,"score_spread":0.03473956891978677,"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."}}