{"id":"W4413053856","doi":"10.1016/j.eswa.2025.129261","title":"Using external knowledge to enhance user preferences for better sequential recommendation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Key Laboratory of Software Engineering of Yunnan Province; Yunnan University; Yunnan Power Grid Company; National Key Research and Development Program of China; People's Government of Yunnan Province; Natural Science Foundation of Yunnan Province","keywords":"Computer science; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002606304,0.000164554,0.0002107919,0.0001805709,0.0002836313,0.0003155742,0.0006226703,0.00006970604,0.000004425053],"category_scores_gemma":[0.000004963998,0.0001350799,0.00004540512,0.0004390534,0.00001681267,0.000333036,0.0001238193,0.00006613245,0.00001467531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001264676,"about_ca_system_score_gemma":0.0001059842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003074124,"about_ca_topic_score_gemma":0.00005896249,"domain_scores_codex":[0.9987131,0.00007194267,0.000364158,0.0005118645,0.0001039928,0.0002348789],"domain_scores_gemma":[0.9989274,0.00007495109,0.0001341003,0.0005752005,0.0002115874,0.00007676751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005793557,0.0006148134,0.002550381,0.0005966042,0.000296983,0.000001024016,0.004778296,0.0001890798,0.04980981,0.469334,0.1157003,0.3560708],"study_design_scores_gemma":[0.0002617225,0.0001020535,0.0001416351,0.0004615098,0.00001255111,0.00001418207,0.0001571557,0.01981094,0.03251907,0.001565783,0.9445469,0.0004064325],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006696132,0.0003097894,0.9924245,0.000901418,0.0004275179,0.002246297,0.000008772256,0.0002508606,0.002761222],"genre_scores_gemma":[0.5190448,0.00000798403,0.4673147,0.0005011907,0.0003911885,0.01128489,0.00001308267,0.00001882872,0.001423315],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8288467,"threshold_uncertainty_score":0.55084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05091744191422129,"score_gpt":0.3741167245093967,"score_spread":0.3231992825951754,"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."}}