{"id":"W2023261081","doi":"10.1016/j.ins.2015.02.029","title":"On personalizing Web search using social network analysis","year":2015,"lang":"en","type":"article","venue":"Information Sciences","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; World Wide Web; Search engine; Relevance (law); The Internet; Personalized search; Information retrieval; Set (abstract data type); Social network (sociolinguistics); Social web; Web search query; Publication; Rank (graph theory); Order (exchange); Web search engine; Social media; Mathematics","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":[0.002615514,0.0006548502,0.001151317,0.003306389,0.0008165097,0.001513905,0.0008289997,0.00132385,0.002066017],"category_scores_gemma":[0.0145774,0.0004028654,0.0007425383,0.003308368,0.0005800939,0.004363994,0.0009159126,0.0008097478,0.0005790005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000750067,"about_ca_system_score_gemma":0.0005995821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008896177,"about_ca_topic_score_gemma":0.01440291,"domain_scores_codex":[0.9984724,0.0007850867,0.00007062748,0.000208552,0.0003840841,0.0000792316],"domain_scores_gemma":[0.9883538,0.009500835,0.0002952418,0.0009017176,0.0008240095,0.0001242699],"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":[0.0007806023,0.001013377,0.02655836,0.0005117707,0.0005197033,0.0001518312,0.0008018494,0.1803484,0.006088589,0.0477986,0.00794087,0.727486],"study_design_scores_gemma":[0.00003302582,0.0001051259,0.003892664,0.00003705785,0.000116323,0.00007449229,0.0001300089,0.965837,0.00169433,0.02589276,0.002156274,0.00003103459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2317966,0.005593997,0.7470539,0.002059385,0.0001992037,0.0002905211,0.00056377,0.0008514471,0.01159129],"genre_scores_gemma":[0.8375412,0.002184035,0.1534346,0.0001574841,0.0003551955,0.000123476,0.0004266888,0.0000789143,0.00569847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008896177,"threshold_uncertainty_score":0.01768881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1534948648603519,"score_gpt":0.3650827558740766,"score_spread":0.2115878910137247,"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."}}