{"id":"W1586356343","doi":"10.1007/11768012_55","title":"An Adaptive Hypermedia System Using a Constraint Satisfaction Approach for Information Personalization","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Personalization; Computer science; Adaptive hypermedia; Viewpoints; Adaptation (eye); Constraint satisfaction problem; Constraint (computer-aided design); Relevance (law); Information retrieval; Constraint satisfaction; Hypermedia; Information filtering system; World Wide Web; Artificial intelligence","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":[0.0009893903,0.0007027632,0.0008424115,0.000851059,0.0009424395,0.002061791,0.002451276,0.001121302,0.008210198],"category_scores_gemma":[0.002983151,0.0005300092,0.0007250704,0.001352918,0.0005073376,0.002392054,0.001947638,0.00123537,0.001576089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000548783,"about_ca_system_score_gemma":0.001054296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009004259,"about_ca_topic_score_gemma":0.01022383,"domain_scores_codex":[0.9991561,0.0001912982,0.00008590916,0.0001925014,0.0003232897,0.00005092819],"domain_scores_gemma":[0.9984319,0.0007112258,0.00006960007,0.0003085192,0.0003728016,0.0001059619],"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":[0.001036329,0.001037251,0.001793309,0.0005265109,0.0002855449,0.0006645868,0.001393375,0.05713282,0.1149844,0.02218383,0.02582398,0.773138],"study_design_scores_gemma":[0.0001492511,0.0001249303,0.0006890506,0.00002582346,0.0001471694,0.0002136696,0.0001994551,0.9238871,0.04454592,0.008712037,0.02121965,0.00008597507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01726907,0.00007402241,0.9668137,0.0001484366,0.00002655628,0.0002320342,0.0002537712,0.01176811,0.003414243],"genre_scores_gemma":[0.1574688,0.0001561484,0.8311207,0.0001774233,0.00004203828,0.0004004694,0.001088342,0.0007191109,0.008827052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009004259,"threshold_uncertainty_score":0.02746588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01958910354462238,"score_gpt":0.2297564954025345,"score_spread":0.2101673918579121,"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."}}