{"id":"W4386791067","doi":"10.1007/s42979-023-02166-5","title":"A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation","year":2023,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Recommender system; Computer science; E-commerce; Product (mathematics); Collaborative filtering; Information retrieval; Revenue; Data science; Data mining; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005095279,0.001131563,0.002712384,0.006257185,0.001454134,0.003414708,0.003181954,0.002072889,0.004749974],"category_scores_gemma":[0.01180258,0.001114978,0.001870191,0.0124209,0.0006455391,0.005666707,0.001206534,0.001660932,0.002485786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001548966,"about_ca_system_score_gemma":0.002346759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00927496,"about_ca_topic_score_gemma":0.01192787,"domain_scores_codex":[0.9957845,0.0009596362,0.0006029832,0.000846089,0.001629931,0.000176841],"domain_scores_gemma":[0.9900606,0.00513377,0.0005010025,0.001457994,0.002626175,0.0002204835],"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":[0.0002146436,0.0004958183,0.008823131,0.003941953,0.0004106887,0.0001347502,0.0003134299,0.00950115,0.002338265,0.03322028,0.01308978,0.927516],"study_design_scores_gemma":[0.0002068833,0.002034763,0.02264279,0.003751097,0.001390877,0.004572505,0.0007138335,0.4467956,0.01088205,0.08501688,0.4213583,0.0006344466],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.03300158,0.2867506,0.6482469,0.002897096,0.0009450933,0.001163164,0.002183153,0.001747427,0.0230651],"genre_scores_gemma":[0.1407867,0.1665705,0.6739193,0.0008708222,0.001357746,0.0006310376,0.002854814,0.0001649035,0.01284419],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.00927496,"threshold_uncertainty_score":0.02694672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1192941130776285,"score_gpt":0.3121814208607136,"score_spread":0.1928873077830851,"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."}}