{"id":"W2783944588","doi":"10.1145/3159652.3159668","title":"Sequential Recommendation with User Memory Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":508,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Computer science; Recommender system; Recurrent neural network; Collaborative filtering; Markov chain; Feature (linguistics); Artificial intelligence; Reading (process); Representation (politics); Information retrieval; Artificial neural network; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0002411906,0.0000860278,0.00008707745,0.00004941857,0.0001015129,0.0001604674,0.000336109,0.00004555162,0.0001929962],"category_scores_gemma":[0.000001772498,0.000059928,0.00002174296,0.0001821162,0.00002793741,0.0005005059,0.0001203765,0.0000630327,0.00004032904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002342365,"about_ca_system_score_gemma":0.00001961959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00014378,"about_ca_topic_score_gemma":0.0001261925,"domain_scores_codex":[0.9993177,0.00005044276,0.0001339761,0.0002372066,0.00008956114,0.0001710887],"domain_scores_gemma":[0.9994555,0.00001694147,0.00005740676,0.0003400297,0.00008079616,0.00004930872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002215411,0.0001019213,0.002324884,0.00001180819,0.00008115832,0.00001056462,0.0005945312,0.0000202498,0.000255049,0.1515983,0.3202799,0.5246995],"study_design_scores_gemma":[0.001009801,0.001307477,0.001820547,0.00007045251,0.00001362563,0.0001872525,0.00008290238,0.2435893,0.03201276,0.002963441,0.7160702,0.0008722142],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000626151,0.000004471527,0.9302183,0.001358887,0.0004343732,0.0001208279,1.492887e-7,0.0004320172,0.0668048],"genre_scores_gemma":[0.8633176,0.000004035246,0.1331069,0.001083942,0.0003847944,0.00002026666,0.000003293624,0.000009110802,0.002070021],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8626915,"threshold_uncertainty_score":0.2443793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929778861318301,"score_gpt":0.2528503456924196,"score_spread":0.2335525570792366,"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."}}