{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001130557,0.0009297034,0.001127815,0.000681794,0.0004892927,0.0008964199,0.002106523,0.001326143,0.002333055],"category_scores_gemma":[0.004976345,0.0007896782,0.0007294849,0.001125208,0.0005828986,0.002628289,0.0007959173,0.001735924,0.0008048046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048338,"about_ca_system_score_gemma":0.0008548122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03074291,"about_ca_topic_score_gemma":0.0422042,"domain_scores_codex":[0.9992486,0.0001818908,0.00005877797,0.000266573,0.0001456222,0.00009861197],"domain_scores_gemma":[0.9981415,0.0009475933,0.0001980847,0.0003524657,0.0002943346,0.00006588747],"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":[0.0006047777,0.0001912205,0.003911348,0.000154439,0.0001999512,0.0002194292,0.0002473299,0.6823778,0.002911539,0.01531757,0.003846172,0.2900184],"study_design_scores_gemma":[0.000009208086,0.00002485994,0.0001654326,0.000005483151,0.00001763183,0.00002005793,0.000005991147,0.9954851,0.0004545703,0.003382764,0.0004218052,0.000007088995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0684636,0.00137274,0.923371,0.0005259718,0.0001219301,0.00007756882,0.0005186546,0.002297983,0.003250523],"genre_scores_gemma":[0.8645535,0.0006810281,0.1242504,0.0003025067,0.0001239726,0.0001749231,0.0006541203,0.00006721894,0.00919239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03074291,"threshold_uncertainty_score":0.06112796,"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."}}