{"id":"W4387341580","doi":"10.1007/s11257-023-09378-7","title":"Digitally nudging users to explore off-profile recommendations: here be dragons","year":2023,"lang":"en","type":"article","venue":"User Modeling and User-Adapted Interaction","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Alpen-Adria-Universität Klagenfurt","keywords":"Computer science; Content (measure theory); Reading (process); Recommender system; World Wide Web; User engagement; Preference; Social media; Nudge theory; Digital content; Information retrieval; Internet privacy; Psychology; Social psychology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003322851,0.000242329,0.0002411118,0.0004661668,0.0003311569,0.0006379422,0.0004085561,0.0001042104,0.00001936935],"category_scores_gemma":[0.00006066629,0.0002391683,0.00008535717,0.0006151553,0.000008819,0.001972669,0.0003149526,0.0002656218,0.00007934721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001073865,"about_ca_system_score_gemma":0.00003790082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003449977,"about_ca_topic_score_gemma":0.00008906361,"domain_scores_codex":[0.998242,0.00007977976,0.0004636732,0.0006008285,0.0002419185,0.0003718588],"domain_scores_gemma":[0.9989063,0.0001080183,0.0001172679,0.000532472,0.0001654213,0.0001705669],"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":[0.0001629612,0.0004365099,0.002493906,0.0002256457,0.0004198916,0.00006889788,0.03213117,0.05840124,0.006077868,0.02167534,0.3331284,0.5447782],"study_design_scores_gemma":[0.0002076432,0.0001201571,0.0000698621,0.0002717153,0.000008019713,0.00002362142,0.003861288,0.8947143,0.0006512073,0.0006193493,0.09909715,0.0003556891],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2491304,0.00004898801,0.7342755,0.01093362,0.001259521,0.0004217147,0.00001224067,0.001835858,0.002082181],"genre_scores_gemma":[0.974766,0.000226521,0.02270816,0.0008893864,0.0001137992,0.0001654078,0.00006575115,0.00004150196,0.001023493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8363131,"threshold_uncertainty_score":0.9753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080302820031851,"score_gpt":0.328750278308122,"score_spread":0.2207199963049369,"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."}}