{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002061089,0.001054259,0.001117333,0.002261007,0.000967857,0.0009869824,0.001473349,0.002740983,0.002205041],"category_scores_gemma":[0.01587817,0.000722467,0.0009788008,0.001275766,0.0007647095,0.002099842,0.001134487,0.001163037,0.0007612391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005391648,"about_ca_system_score_gemma":0.001239696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003419763,"about_ca_topic_score_gemma":0.007649608,"domain_scores_codex":[0.9981818,0.0008437437,0.0001099513,0.0003905271,0.0003665322,0.0001075281],"domain_scores_gemma":[0.9906764,0.007448468,0.0004430684,0.0004754246,0.0008054606,0.0001511863],"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":[0.001053678,0.0007993199,0.0276665,0.0009168746,0.0004684648,0.0005268513,0.002784978,0.1212913,0.008119075,0.01632498,0.01647517,0.8035728],"study_design_scores_gemma":[0.0002676374,0.0003405726,0.005024997,0.0001655376,0.0002695986,0.0004775788,0.0008011521,0.9372692,0.006300387,0.04333578,0.005657199,0.00009034454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1982854,0.001522634,0.7906386,0.002726662,0.0001554682,0.0005255821,0.000442021,0.001598122,0.004105435],"genre_scores_gemma":[0.5793667,0.0003687365,0.4164485,0.0004182761,0.0001424303,0.0003100131,0.0008017754,0.00009267802,0.002050939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003419763,"threshold_uncertainty_score":0.0109002,"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."}}