{"id":"W4403459152","doi":"10.3390/electronics13204073","title":"UDIS: Enhancing Collaborative Filtering with Fusion of Dimensionality Reduction and Semantic Similarity","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Dimensionality reduction; Similarity (geometry); Collaborative filtering; Semantic similarity; Computer science; Fusion; Artificial intelligence; Reduction (mathematics); Information retrieval; Pattern recognition (psychology); Natural language processing; Mathematics; Recommender system; Linguistics; Philosophy","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":[0.004831251,0.001278579,0.002785648,0.003416881,0.001014643,0.001675247,0.00160311,0.001293709,0.001121883],"category_scores_gemma":[0.009973217,0.0004462452,0.002048573,0.003703841,0.000502833,0.002250241,0.001955318,0.001032199,0.0007373955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007960207,"about_ca_system_score_gemma":0.001229914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01067904,"about_ca_topic_score_gemma":0.01005816,"domain_scores_codex":[0.9965516,0.001127385,0.0002664383,0.0005619602,0.001313007,0.0001795942],"domain_scores_gemma":[0.9960552,0.001751886,0.0002479581,0.0006577453,0.001169849,0.0001173633],"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":[0.0005546802,0.000566423,0.007198439,0.0003276161,0.0008373288,0.0001302536,0.0005353092,0.1069286,0.009598655,0.009230589,0.008212886,0.8558791],"study_design_scores_gemma":[0.00004596022,0.0001920639,0.001947131,0.00002364794,0.0001302804,0.000111214,0.00009877035,0.9836138,0.003874029,0.006110467,0.003805739,0.00004682078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02008448,0.0009001083,0.9755394,0.0001905857,0.0001285599,0.0001319721,0.0002217391,0.001501823,0.001301249],"genre_scores_gemma":[0.2737103,0.0006606497,0.7215713,0.0002583054,0.0002108733,0.0002089505,0.001159301,0.0001119234,0.002108414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01067904,"threshold_uncertainty_score":0.02555043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008647927301655057,"score_gpt":0.2466911802071753,"score_spread":0.2380432529055202,"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."}}