{"id":"W2064122758","doi":"10.1002/meet.1450430154","title":"An adaptive user profile for filtering news based on a user interest hierarchy","year":2006,"lang":"en","type":"article","venue":"Proceedings of the American Society for Information Science and Technology","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Session (web analytics); Computer science; Hierarchy; User modeling; Ranking (information retrieval); User profile; Recall; Computer user satisfaction; Human–computer interaction; Information retrieval; User interface; World Wide Web; Programming language; Psychology","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":[0.003036886,0.000727663,0.001037799,0.001964416,0.0004488935,0.001186351,0.001134518,0.0008505721,0.001464891],"category_scores_gemma":[0.007613378,0.0004387389,0.0004383399,0.0007375256,0.000262524,0.001848414,0.0004724537,0.0007882565,0.00125642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007031712,"about_ca_system_score_gemma":0.0004398788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00576909,"about_ca_topic_score_gemma":0.006678677,"domain_scores_codex":[0.9986443,0.0005690659,0.00009544262,0.0002543135,0.0003322971,0.0001045895],"domain_scores_gemma":[0.994206,0.002717047,0.0004292097,0.00071907,0.001429364,0.0004994047],"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":[0.005895541,0.002926094,0.07258881,0.0005083335,0.0003794964,0.0005042035,0.001986285,0.02490122,0.1066583,0.003957046,0.01217147,0.7675231],"study_design_scores_gemma":[0.0001966215,0.001192387,0.02771108,0.00003802464,0.0001673405,0.0005180204,0.000199517,0.9384876,0.025587,0.002008148,0.003738094,0.0001560443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4012893,0.0003742746,0.5711529,0.0003738235,0.00004463819,0.0008249026,0.000831853,0.02226734,0.002840857],"genre_scores_gemma":[0.8417994,0.00008089469,0.1554179,0.00009419706,0.00002858119,0.0001874372,0.0007369487,0.0001371132,0.001517456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00576909,"threshold_uncertainty_score":0.01606077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01962902791533398,"score_gpt":0.2743648131742228,"score_spread":0.2547357852588888,"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."}}