{"id":"W3197544447","doi":"10.1145/3472163.3472183","title":"A Framework for Enhancing Deep Learning Based Recommender Systems with Knowledge Graphs","year":2021,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Recommender system; Pipeline (software); Deep learning; Collaborative filtering; Information retrieval; Domain knowledge; Artificial intelligence; Information filtering system; Graph; Information extraction; Machine learning; Data mining; Theoretical computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001002384,0.0008280812,0.0006377478,0.001052583,0.0005573608,0.001290071,0.001806881,0.001088064,0.003438119],"category_scores_gemma":[0.003227899,0.000518073,0.001056878,0.001250423,0.0006928094,0.00258506,0.001329839,0.002220366,0.001325488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001190364,"about_ca_system_score_gemma":0.001344178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01532362,"about_ca_topic_score_gemma":0.03271088,"domain_scores_codex":[0.9994356,0.0001481127,0.00004258758,0.0001517921,0.0001721351,0.00004975851],"domain_scores_gemma":[0.9989504,0.0003972832,0.00006828562,0.0002826144,0.0002326999,0.00006882014],"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":[0.0001430033,0.0003398249,0.001304822,0.0004257873,0.0002634926,0.0002734804,0.0002927714,0.3773191,0.01132196,0.1573379,0.01606799,0.4349099],"study_design_scores_gemma":[0.00002135541,0.00005392508,0.0002296205,0.00003555162,0.00005377327,0.00006603278,0.00002615033,0.9111274,0.003408661,0.06832624,0.01662926,0.0000221705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001763355,0.0001665868,0.9949462,0.0002079515,0.00002648188,0.00004135359,0.0001726292,0.00146642,0.001209133],"genre_scores_gemma":[0.09114513,0.0005505055,0.9027309,0.0002707354,0.00004785541,0.0001570142,0.0008286388,0.000125811,0.004143381],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01532362,"threshold_uncertainty_score":0.03046882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02870973243417235,"score_gpt":0.2841755681988818,"score_spread":0.2554658357647094,"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."}}