{"id":"W2949517518","doi":"10.48550/arxiv.1809.07426","title":"Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sequence (biology); Computer science; Embedding; Variety (cybernetics); Order (exchange); Recommender system; Convolutional neural network; Information retrieval; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005826509,0.0003723446,0.000388817,0.0003015515,0.0003000616,0.0002450778,0.001618246,0.000386628,0.0002880647],"category_scores_gemma":[0.00002179633,0.000435282,0.000276834,0.0003968927,0.0001764126,0.0007206224,0.001829639,0.0005177064,0.000114206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006193991,"about_ca_system_score_gemma":0.0002879043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004334729,"about_ca_topic_score_gemma":0.00002453761,"domain_scores_codex":[0.9975085,0.0003179954,0.0003304339,0.001286558,0.0001375619,0.0004189863],"domain_scores_gemma":[0.9980753,0.00007930393,0.0004454201,0.0009091144,0.0003157894,0.0001750834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000122092,0.0003574557,0.00626254,0.0004551718,0.0007536799,0.0004757659,0.00171243,0.006609438,0.00142728,0.9463832,0.02436071,0.0110802],"study_design_scores_gemma":[0.0005205549,0.00008732471,0.0001573004,0.0001720587,0.0000491091,0.0000508837,0.00004449402,0.8981795,0.0007008707,0.0869369,0.01235757,0.0007434835],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02180076,0.00002776197,0.971109,0.0003611273,0.001709681,0.0003754239,0.00003480524,0.0006178255,0.003963585],"genre_scores_gemma":[0.9822409,0.00007572486,0.0158237,0.0001839334,0.0003078835,0.000004730601,0.0001061555,0.00002115136,0.001235826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9604402,"threshold_uncertainty_score":0.9998099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1328237490058722,"score_gpt":0.2486300469546071,"score_spread":0.1158062979487349,"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."}}