{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003402512,0.001567254,0.003742539,0.00380177,0.001058096,0.002096733,0.00264055,0.002017269,0.005648627],"category_scores_gemma":[0.01501047,0.001103399,0.002030908,0.004430401,0.0004704455,0.002823864,0.001570111,0.001918003,0.00245202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005158945,"about_ca_system_score_gemma":0.001034024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005584282,"about_ca_topic_score_gemma":0.01392798,"domain_scores_codex":[0.9971784,0.0007290925,0.000267609,0.0004975126,0.00115816,0.0001691764],"domain_scores_gemma":[0.9904104,0.005351823,0.0003878975,0.001452041,0.00215437,0.0002434515],"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":[0.001428979,0.0009778918,0.006967639,0.0004899677,0.0008860526,0.0002599311,0.0003266746,0.09240169,0.01930595,0.003526289,0.01637252,0.8570564],"study_design_scores_gemma":[0.0001374024,0.0004213178,0.004211227,0.00008858166,0.0004344842,0.0003465357,0.0001240956,0.9794388,0.005423297,0.005225591,0.004051567,0.00009720334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1335338,0.004718229,0.8522383,0.0005413047,0.0008239708,0.0004581992,0.001120891,0.002078797,0.004486531],"genre_scores_gemma":[0.4173576,0.001093914,0.5710676,0.0002912136,0.0005709912,0.0004384436,0.002128276,0.0002452197,0.006806806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005648627,"threshold_uncertainty_score":0.01889652,"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."}}