{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004114551,0.0007524445,0.0003149183,0.001167748,0.0001955792,0.000340918,0.0004477766,0.0004850712,0.0005885041],"category_scores_gemma":[0.001528698,0.0002405481,0.0005459337,0.0008342111,0.0001639467,0.0006676217,0.0002765312,0.000667132,0.000275672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007010845,"about_ca_system_score_gemma":0.0003876043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01784765,"about_ca_topic_score_gemma":0.03161308,"domain_scores_codex":[0.9998093,0.00004260798,0.00001548672,0.00006606135,0.00003358648,0.00003295317],"domain_scores_gemma":[0.9994512,0.0002868281,0.00007669358,0.00005540556,0.0001046467,0.00002523612],"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":[0.0009083012,0.001826392,0.2367985,0.000357725,0.0007240865,0.000844145,0.0005393281,0.210864,0.02525962,0.002523502,0.0110183,0.5083361],"study_design_scores_gemma":[0.000006670151,0.00004668279,0.01807557,0.0000088375,0.00004021001,0.0000407164,0.00006908672,0.9773183,0.002869418,0.0009459069,0.0005688764,0.000009660329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.886843,0.0006591588,0.1047074,0.0004661402,0.00004952121,0.0001090599,0.002402107,0.001124327,0.003639353],"genre_scores_gemma":[0.9678105,0.0001668995,0.02768467,0.00007651185,0.00001668071,0.00005929244,0.002664326,0.00001716622,0.001503893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01784765,"threshold_uncertainty_score":0.03548753,"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."}}