{"id":"W2196791692","doi":"10.1609/aaai.v29i1.9529","title":"Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network","year":2015,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Convolutional neural network; Domain (mathematical analysis); Social media; Domain adaptation; Consumption (sociology); Sentence; Adaptation (eye); Product (mathematics); Representation (politics); Task (project management); Targeted advertising; Artificial intelligence; Social network (sociolinguistics); Artificial neural network; Media consumption; Machine learning; Human–computer interaction; World Wide Web; Advertising; Psychology; Business","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":[0.0007614394,0.000220938,0.0002977037,0.00009407956,0.0002498254,0.0002547531,0.001126527,0.0001348923,0.00002451125],"category_scores_gemma":[0.0001619569,0.0001790524,0.0001238621,0.000373781,0.0002546459,0.000633263,0.0004090973,0.0002578119,0.00002131657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001305048,"about_ca_system_score_gemma":0.0001051755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001592917,"about_ca_topic_score_gemma":0.00004508357,"domain_scores_codex":[0.997988,0.00006400984,0.0005911051,0.0004489272,0.0005695255,0.0003384624],"domain_scores_gemma":[0.9982644,0.0001362974,0.0005611226,0.0001699688,0.0007638779,0.0001043532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001098878,0.00007845321,0.002823813,0.0000105267,0.00003310505,7.864033e-7,0.004647939,0.00007531361,0.005681302,0.9697691,0.00120157,0.01556821],"study_design_scores_gemma":[0.0001207719,0.0001880965,0.00216704,0.000445444,0.00003068084,0.00001357841,0.003481136,0.3442165,0.04253034,0.6061317,0.0001842375,0.0004904151],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6997405,0.00005934666,0.2950255,0.001312353,0.001796867,0.0004067852,0.00001142237,0.0001846736,0.0014625],"genre_scores_gemma":[0.9703271,0.000005080312,0.02919338,0.0001070553,0.0003178247,0.00001952415,0.000001983849,0.00001141117,0.00001668352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3636373,"threshold_uncertainty_score":0.7301543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2510960113646989,"score_gpt":0.323205783877797,"score_spread":0.07210977251309808,"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."}}