{"id":"W2783272285","doi":"10.1145/3159652.3159656","title":"Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1909,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Sequence (biology); Embedding; Recommender system; Variety (cybernetics); Order (exchange); Sequential Pattern Mining; Artificial intelligence; Convolutional neural network; Information retrieval; Data mining","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":[0.0007614691,0.0009095278,0.001103946,0.0007313361,0.0003104942,0.0005977246,0.001428083,0.00086794,0.001864573],"category_scores_gemma":[0.002527311,0.0005135708,0.0007184874,0.001101749,0.0003682351,0.001908232,0.0005894435,0.001117321,0.0008554738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009068077,"about_ca_system_score_gemma":0.0009197534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02663207,"about_ca_topic_score_gemma":0.04731979,"domain_scores_codex":[0.9995198,0.0001097425,0.0000248645,0.0001504203,0.0001316381,0.0000634534],"domain_scores_gemma":[0.9990031,0.0003878216,0.0001120009,0.0002723965,0.0001645383,0.00006020161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004041967,0.000344625,0.00572372,0.0001491638,0.0002653341,0.0002029134,0.0001128399,0.6047461,0.006973391,0.01207032,0.006868978,0.3621385],"study_design_scores_gemma":[0.000005825079,0.00003470482,0.0002895533,0.000003822647,0.00001206784,0.00003284342,0.00000403908,0.9966031,0.0005857659,0.002083115,0.0003391989,0.000005897448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1069265,0.001133987,0.8844802,0.0003890632,0.00008739538,0.00009569604,0.0006721006,0.002448592,0.00376642],"genre_scores_gemma":[0.8286978,0.0006057128,0.1596422,0.0001807899,0.00007684685,0.00009436643,0.001307237,0.00008840785,0.009306571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02663207,"threshold_uncertainty_score":0.05295414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07371477418994596,"score_gpt":0.3446337966476576,"score_spread":0.2709190224577117,"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."}}