{"id":"W3139779074","doi":"10.1109/asonam49781.2020.9381352","title":"Semantics Embedded Sequential Recommendation for E-Commerce Products (SEMSRec)","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; University of Windsor","keywords":"Computer science; Semantics (computer science); Recommender system; Collaborative filtering; Information retrieval; Process (computing); Similarity (geometry); Set (abstract data type); Product (mathematics); Semantic similarity; Cluster analysis; E-commerce; Process mining; World Wide Web; Data mining; Artificial intelligence; Work in process","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003403804,0.0001493146,0.0002024787,0.00004934238,0.0001160566,0.0002179397,0.0005874684,0.00006618324,0.00003069283],"category_scores_gemma":[0.00007549238,0.0001325116,0.00007100252,0.0002973054,0.00001122289,0.0005323049,0.0002089099,0.00009484596,0.00003227957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002658102,"about_ca_system_score_gemma":0.00005100013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002317267,"about_ca_topic_score_gemma":0.000004919737,"domain_scores_codex":[0.9987555,0.0000709946,0.0003232333,0.000470296,0.0001276576,0.0002523352],"domain_scores_gemma":[0.9992037,0.00006002429,0.0001199959,0.0003552905,0.0001522318,0.000108718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002200576,0.0001204022,0.0001666533,0.0002853671,0.00007079312,0.000003014695,0.002531971,0.000006281753,0.01082317,0.1779617,0.6244085,0.1836002],"study_design_scores_gemma":[0.0006738441,0.0004189599,0.00006526323,0.00002262099,0.00001388659,0.0000174247,0.0001153185,0.1203235,0.1252939,0.003545812,0.7490255,0.0004840512],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003048757,0.00001268954,0.906831,0.08594958,0.0005399031,0.0006490914,0.000004242847,0.0007015808,0.005007041],"genre_scores_gemma":[0.4904144,0.00001652562,0.5011293,0.006969036,0.0005669018,0.0001204751,0.00004299286,0.00002687551,0.0007134874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4901096,"threshold_uncertainty_score":0.5403668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08468480100821477,"score_gpt":0.2974732456513053,"score_spread":0.2127884446430905,"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."}}