{"id":"W2604334159","doi":"10.1609/aaai.v31i1.10758","title":"Low-Rank Linear Cold-Start Recommendation from Social Data","year":2017,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scalability; Cold start (automotive); Metadata; Collaborative filtering; Weighting; Hyperparameter; Rank (graph theory); Information retrieval; Recommender system; Data mining; Machine learning; Artificial intelligence; World Wide Web; Mathematics","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":[0.004775673,0.002428359,0.003988666,0.001655594,0.001458872,0.002357357,0.004486744,0.003107758,0.00270337],"category_scores_gemma":[0.01323926,0.00151599,0.001830596,0.002718671,0.00186015,0.003624326,0.002034633,0.003901905,0.002945095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001584358,"about_ca_system_score_gemma":0.001849781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0183058,"about_ca_topic_score_gemma":0.03730046,"domain_scores_codex":[0.9957215,0.001760043,0.0002106306,0.001163979,0.0008186328,0.0003252507],"domain_scores_gemma":[0.9900147,0.005905679,0.0007450033,0.001740966,0.001287379,0.0003062586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009467119,0.0006576445,0.006654905,0.0007849056,0.0005905446,0.0003647229,0.0006655267,0.7287252,0.00795617,0.02471583,0.02484458,0.2030932],"study_design_scores_gemma":[0.00003264493,0.00008209105,0.0004359721,0.00002459518,0.00003077404,0.00004524942,0.00003875754,0.9879416,0.001029681,0.009069055,0.001242807,0.00002675108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02406154,0.0009776691,0.9708642,0.0006613347,0.0000938606,0.0001454809,0.0006615615,0.001150451,0.001383832],"genre_scores_gemma":[0.548507,0.001208503,0.4224989,0.001456433,0.0007238237,0.0008257162,0.006568977,0.0003603634,0.01785025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0183058,"threshold_uncertainty_score":0.03639853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2202685102135068,"score_gpt":0.3577547114737828,"score_spread":0.1374862012602761,"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."}}