{"id":"W4406063205","doi":"10.1016/j.engappai.2024.109936","title":"DSRS: DELIGHT sequential recommender system","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Ningbo Municipal Bureau of Science and Technology","keywords":"Computer science; Recommender system; Artificial intelligence; Information retrieval","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":[0.001583577,0.001378413,0.001658278,0.001105627,0.0007136018,0.000874589,0.002457517,0.001573243,0.00571362],"category_scores_gemma":[0.003516376,0.0006863147,0.0009761205,0.001301195,0.0003643449,0.001752623,0.00106248,0.001771288,0.003878413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008714615,"about_ca_system_score_gemma":0.00185565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05533525,"about_ca_topic_score_gemma":0.07941616,"domain_scores_codex":[0.9990022,0.0002215201,0.00008139663,0.0003373457,0.0002704249,0.0000871399],"domain_scores_gemma":[0.998781,0.0004198279,0.0000880438,0.0002827549,0.0003394771,0.0000889755],"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":[0.001214699,0.0005846134,0.0103304,0.0005607089,0.0005864413,0.0006021117,0.0002830847,0.2348597,0.009290789,0.01045168,0.06585583,0.66538],"study_design_scores_gemma":[0.0001217582,0.0001634495,0.0008684152,0.00002022911,0.00007118854,0.0002006506,0.00002088838,0.9814984,0.001285164,0.003862226,0.01183646,0.00005111733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0346759,0.003656697,0.9310405,0.001389735,0.0004799141,0.0006421575,0.003994293,0.01728285,0.006837945],"genre_scores_gemma":[0.4656921,0.003079192,0.4971051,0.001186387,0.0003963412,0.0006055081,0.007542478,0.0003559624,0.02403695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05533525,"threshold_uncertainty_score":0.1100264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02201911927013266,"score_gpt":0.2798424940936194,"score_spread":0.2578233748234868,"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."}}