{"id":"W1128302166","doi":"10.1609/aaai.v28i1.8706","title":"Who Also Likes It? Generating the Most Persuasive Social Explanations in Recommender Systems","year":2014,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; National Science Foundation","keywords":"Recommender system; Computer science; Statement (logic); Set (abstract data type); Persuasive technology; Social network (sociolinguistics); Persuasion; Information retrieval; World Wide Web; Social media; Psychology; Social psychology","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":[0.00136133,0.0002270836,0.0003133713,0.00015057,0.0005007973,0.0006164574,0.002053527,0.0001118935,0.00001317024],"category_scores_gemma":[0.0003158782,0.0001466147,0.00009701643,0.0006711794,0.0001448264,0.0004323905,0.0003484926,0.0003646603,0.00001585757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007376076,"about_ca_system_score_gemma":0.00007036894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003566394,"about_ca_topic_score_gemma":0.000117951,"domain_scores_codex":[0.9980118,0.00009979038,0.0007054798,0.0004431054,0.000388266,0.0003515691],"domain_scores_gemma":[0.9984336,0.0002250567,0.0004862475,0.0002954754,0.0005097789,0.00004984656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006144541,0.00006015838,0.0001899585,0.0000297726,0.00001243866,1.128411e-7,0.00633614,0.00005837818,0.003212332,0.9520305,0.003862994,0.03420104],"study_design_scores_gemma":[0.00009208747,0.0003144703,0.0003677678,0.0008264822,0.00001940716,0.00001541143,0.01511296,0.7571562,0.1137387,0.1056142,0.006024843,0.0007174838],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1264675,0.0001909352,0.5657788,0.1616999,0.004216438,0.003891032,0.00002537038,0.0006607954,0.1370692],"genre_scores_gemma":[0.9975789,0.00003270285,0.001166818,0.0006726338,0.0001994143,0.0001236058,5.516658e-7,0.00001281727,0.0002125269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8711114,"threshold_uncertainty_score":0.5978774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0958385672770619,"score_gpt":0.3096257581721414,"score_spread":0.2137871908950795,"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."}}