{"id":"W4200199191","doi":"10.1145/3476106","title":"Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Wilfrid Laurier University","funders":"Wilfrid Laurier University","keywords":"Latent Dirichlet allocation; Computer science; Personalization; Topic model; Information retrieval; Web page; Graph; Probabilistic logic; Web search query; Personalized search; User modeling; World Wide Web; User interface; Data mining; Search engine; Theoretical computer science; Artificial intelligence","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":[0.002003237,0.0009793871,0.001552869,0.002352366,0.0007116053,0.001756346,0.001706394,0.001715639,0.002126683],"category_scores_gemma":[0.007315936,0.0007998366,0.001822932,0.002901858,0.001144611,0.003854798,0.001002087,0.002158641,0.001188936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001644537,"about_ca_system_score_gemma":0.000913033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01414199,"about_ca_topic_score_gemma":0.01594034,"domain_scores_codex":[0.9984011,0.0006562373,0.00007684482,0.0004864426,0.000237467,0.0001418439],"domain_scores_gemma":[0.996608,0.002445708,0.0002347961,0.0002902108,0.000320195,0.0001011785],"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":[0.001019,0.0004851757,0.01814567,0.0006843683,0.0006278874,0.000562771,0.001602853,0.5951419,0.007822831,0.1085777,0.01469386,0.2506361],"study_design_scores_gemma":[0.00002157369,0.00003280854,0.001406848,0.00001711935,0.00006911063,0.0001249872,0.00004594796,0.9646057,0.0003243388,0.03157504,0.001746307,0.00003016561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06286655,0.003430642,0.9265,0.001558852,0.0001133004,0.0001817774,0.001095643,0.001018711,0.00323445],"genre_scores_gemma":[0.873003,0.002562051,0.1130791,0.0005478687,0.000349166,0.0004305775,0.001693011,0.0001971561,0.00813807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01414199,"threshold_uncertainty_score":0.02811933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1164168471323905,"score_gpt":0.3228678552276038,"score_spread":0.2064510080952133,"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."}}