{"id":"W2740305962","doi":"10.24963/ijcai.2017/485","title":"Learning Discriminative Recommendation Systems with Side Information","year":2017,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Canada Research Chairs","keywords":"Recommender system; Computer science; Discriminative model; Exploit; Information retrieval; Joint (building); Collaborative filtering; Machine learning; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003258476,0.00008449726,0.0001031124,0.00006816041,0.0004909962,0.001359522,0.0004894636,0.00003222354,0.000004623676],"category_scores_gemma":[0.0000349534,0.00005776433,0.0000174734,0.00003831706,0.00001729973,0.004680699,0.0001554559,0.00009537474,0.0000301822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000375638,"about_ca_system_score_gemma":0.00001833439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000721207,"about_ca_topic_score_gemma":0.00002843317,"domain_scores_codex":[0.9994071,0.00005166862,0.0001706818,0.0001231302,0.0001226058,0.0001248302],"domain_scores_gemma":[0.9991336,0.00002847283,0.0003025257,0.0004021419,0.0001022504,0.0000309484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007057949,0.00002570605,0.005228566,0.00007547273,0.00003665669,0.000002927244,0.003389194,0.00007481232,0.0000329809,0.2623568,0.005599783,0.72317],"study_design_scores_gemma":[0.001480025,0.001401679,0.04033845,0.0004611034,0.00001885432,0.0001524287,0.004795086,0.490853,0.005663371,0.002876299,0.4507981,0.001161525],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007028332,0.000002380883,0.8240184,0.001520364,0.0002017944,0.0001714831,3.799385e-7,0.0002543641,0.173128],"genre_scores_gemma":[0.9842682,0.000006029402,0.01487142,0.00007049834,0.00002573342,0.00004180728,0.000006577445,0.000003365988,0.0007063094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9835654,"threshold_uncertainty_score":0.9996772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02119845828635439,"score_gpt":0.261173890697981,"score_spread":0.2399754324116266,"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."}}