{"id":"W1978384613","doi":"10.1145/1651587.1651595","title":"A session generalization technique for improved web usage mining","year":2009,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Generalization; Computer science; Session (web analytics); Scalability; Hierarchy; Set (abstract data type); Sample (material); Data mining; Quality (philosophy); Theoretical computer science; Information retrieval; Machine learning; World Wide Web; Database; Mathematics; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002983053,0.00103219,0.001483319,0.003079205,0.0008911255,0.0006349125,0.002217685,0.001069767,0.001249832],"category_scores_gemma":[0.01001268,0.0005636933,0.002085423,0.003459441,0.0005029584,0.001965602,0.001272286,0.002148819,0.0008155198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005885019,"about_ca_system_score_gemma":0.001181875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007207433,"about_ca_topic_score_gemma":0.01265665,"domain_scores_codex":[0.9977571,0.0007438543,0.0001537322,0.0006087399,0.0005839493,0.0001525984],"domain_scores_gemma":[0.9945234,0.002385677,0.0004048959,0.001763364,0.0007645932,0.000158099],"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":[0.0003299339,0.0006049183,0.03620256,0.0002375327,0.0006543194,0.0004649957,0.001355842,0.09454379,0.01984685,0.01092669,0.01111348,0.8237191],"study_design_scores_gemma":[0.00002588617,0.0002172437,0.01130871,0.00003880757,0.0001213596,0.0007308485,0.0001429608,0.9605023,0.005914512,0.01378197,0.007141632,0.00007389709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03714026,0.0003218143,0.9583384,0.0001285592,0.00003966166,0.0001616571,0.0004725328,0.002774358,0.0006228046],"genre_scores_gemma":[0.3908788,0.0003941887,0.6022627,0.0001924687,0.000147147,0.0005295851,0.002763277,0.0003024959,0.002529302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007207433,"threshold_uncertainty_score":0.01577604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232101134049451,"score_gpt":0.2839753354645008,"score_spread":0.2607652220595557,"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."}}