{"id":"W1594881282","doi":"10.1007/11577935_9","title":"Scaling Down Candidate Sets Based on the Temporal Feature of Items for Improved Hybrid Recommendations","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"MovieLens; Computer science; Recommender system; Context (archaeology); Scalability; Feature (linguistics); Information overload; The Internet; Data mining; Information retrieval; Scale (ratio); Artificial intelligence; Data science; World Wide Web; Collaborative filtering; Database","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001635116,0.0004952068,0.0005690308,0.000602467,0.0003717396,0.0004471989,0.00293776,0.0002366709,0.00001133015],"category_scores_gemma":[0.00009970519,0.0003450964,0.0002363701,0.0003850642,0.000280749,0.0003365361,0.0004706543,0.0006908484,0.000002668943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002450684,"about_ca_system_score_gemma":0.0004398235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000678432,"about_ca_topic_score_gemma":0.0001124695,"domain_scores_codex":[0.9971558,0.00006463947,0.0006194928,0.001123331,0.0005075072,0.000529189],"domain_scores_gemma":[0.9961395,0.001245487,0.0006030418,0.001605651,0.0002936473,0.0001126817],"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":[0.00001549511,0.00005552144,0.00002185533,0.00006666437,0.00002106662,0.000005657792,0.0003169497,0.003850374,0.0001819388,0.01238807,0.003502183,0.9795742],"study_design_scores_gemma":[0.0003161539,0.0002693986,0.00001219274,0.0007541651,0.000008964692,0.00002126699,2.75439e-7,0.9179185,0.005272835,0.04165059,0.03325772,0.0005179319],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001693578,0.0001291613,0.9771143,0.01861311,0.001323772,0.001185036,0.00008354157,0.0001503021,0.001383837],"genre_scores_gemma":[0.2268849,0.00003336581,0.766704,0.005245105,0.0005562174,0.0001062831,0.00005980187,0.00005788177,0.0003524984],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9790563,"threshold_uncertainty_score":0.9999001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02038584737484614,"score_gpt":0.2658168610156339,"score_spread":0.2454310136407878,"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."}}