{"id":"W4416079291","doi":"10.1051/ro/2025151","title":"Addressing the cold start problem in privacy preserving content-based recommender systems using hypercube graphs","year":2025,"lang":"","type":"article","venue":"RAIRO. Operations research","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Cold start (automotive); Recommender system; Collaborative filtering; Hypercube; Service (business); Simple (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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01187544,0.0005530796,0.0008210368,0.002037269,0.0036722,0.006434234,0.003767063,0.0004142294,0.00007312292],"category_scores_gemma":[0.0006095995,0.0004422597,0.000229706,0.005491114,0.0004335526,0.001766759,0.002099556,0.002180596,0.00001840994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204874,"about_ca_system_score_gemma":0.002933292,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01945947,"about_ca_topic_score_gemma":0.002251217,"domain_scores_codex":[0.987088,0.006125029,0.001931605,0.001483557,0.001630995,0.001740842],"domain_scores_gemma":[0.9935449,0.001347533,0.0001541618,0.002805615,0.001899148,0.0002486856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00026706,0.004849378,0.02856079,0.006186171,0.001068404,0.0002072662,0.019895,0.154529,0.1155443,0.52718,0.09982821,0.04188436],"study_design_scores_gemma":[0.001252653,0.0001846443,0.000661899,0.004456732,0.00002215134,0.00001670775,0.00266931,0.9657244,0.004629211,0.000369177,0.0195049,0.0005082237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07785576,0.01258821,0.8265987,0.05050417,0.00278546,0.01776577,0.00008041425,0.0004821417,0.01133935],"genre_scores_gemma":[0.9657786,0.0002577474,0.02736029,0.0004896459,0.0001394564,0.001614133,0.00001425644,0.00006318701,0.00428268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8879228,"threshold_uncertainty_score":0.9998029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4982215150369809,"score_gpt":0.4479984972960612,"score_spread":0.0502230177409197,"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."}}