{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006588902,0.0009466445,0.00114182,0.0007390213,0.0003253307,0.0006135505,0.00151751,0.0009036056,0.002054741],"category_scores_gemma":[0.002309303,0.000552188,0.0007045271,0.001239494,0.0003837016,0.001989892,0.0006187686,0.001120216,0.0009677496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008789322,"about_ca_system_score_gemma":0.0009460981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02509168,"about_ca_topic_score_gemma":0.05001752,"domain_scores_codex":[0.9995469,0.000100354,0.00002327639,0.000147241,0.0001216834,0.00006050832],"domain_scores_gemma":[0.9990677,0.0003526957,0.0001020912,0.0002716123,0.0001471485,0.00005867594],"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":[0.0004141791,0.0003928872,0.005737632,0.0001643118,0.0002694361,0.0002128723,0.000125127,0.5538483,0.007078731,0.01290955,0.009553227,0.4092937],"study_design_scores_gemma":[0.000006503342,0.00003246184,0.0002765523,0.000004045861,0.00001211287,0.00003607161,0.000004687319,0.9959618,0.0006020281,0.002645014,0.0004122009,0.000006494479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1066253,0.001237636,0.8834984,0.0004549366,0.0001043088,0.0000925132,0.0007955831,0.002829208,0.004362062],"genre_scores_gemma":[0.8131488,0.0006754019,0.1731284,0.0002161529,0.00009055649,0.0000934799,0.001509085,0.0001023441,0.01103583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02509168,"threshold_uncertainty_score":0.04989123,"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."}}