{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009888965,0.001159082,0.001145114,0.00255883,0.0004698116,0.0006276533,0.0008790771,0.0008381371,0.002210788],"category_scores_gemma":[0.002530698,0.000489987,0.001563664,0.003660693,0.0003607731,0.001635465,0.0006145135,0.0007824901,0.001236491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004681569,"about_ca_system_score_gemma":0.001166991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01200879,"about_ca_topic_score_gemma":0.03288662,"domain_scores_codex":[0.998782,0.0002229184,0.0001114275,0.0003011096,0.0005113691,0.00007119072],"domain_scores_gemma":[0.998298,0.0006191786,0.0001753495,0.0003012486,0.000562181,0.00004413423],"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":[0.0004423156,0.0005258251,0.01205871,0.0008529357,0.0005834553,0.0004023874,0.000267597,0.06361828,0.01308443,0.01486857,0.01249625,0.8807994],"study_design_scores_gemma":[0.00008877198,0.0004958765,0.008286415,0.00008483171,0.0002284558,0.0009305709,0.000120013,0.9481379,0.007881009,0.01602958,0.01762404,0.00009257467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04765665,0.002503836,0.942091,0.0003391118,0.0001550309,0.0002872024,0.001379705,0.002423227,0.003164229],"genre_scores_gemma":[0.3065037,0.001664736,0.6817333,0.0002582104,0.0001482387,0.0002667681,0.004203529,0.0001207748,0.005100696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01200879,"threshold_uncertainty_score":0.0238778,"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."}}