{"id":"W4393285750","doi":"10.1109/tmm.2024.3382889","title":"SPACE: Self-Supervised Dual Preference Enhancing Network for Multimodal Recommendation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Key Research and Development Projects of Shaanxi Province; Fundamental Research Funds for the Central Universities; State Key Laboratory of Integrated Services Networks; National Natural Science Foundation of China","keywords":"Computer science; Preference; Modality (human–computer interaction); Recommender system; Artificial intelligence; Dual (grammatical number); Space (punctuation); Machine learning; Representation (politics); Task (project management); Information retrieval; Human–computer interaction; Natural language processing","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":[0.0005142485,0.0002555521,0.0002373395,0.0001977363,0.0002721859,0.0003151352,0.000335327,0.0001661029,0.0000670587],"category_scores_gemma":[0.000006618144,0.0002377857,0.0001788926,0.0004251089,0.00001810338,0.00069437,0.000004660941,0.0003245061,0.00009436168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001369917,"about_ca_system_score_gemma":0.0001073968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008368608,"about_ca_topic_score_gemma":0.0001189401,"domain_scores_codex":[0.9982522,0.0001044258,0.0003914755,0.0006244481,0.0002021446,0.0004253192],"domain_scores_gemma":[0.9987025,0.0006077261,0.00005738933,0.0004094742,0.00008757449,0.0001353539],"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":[0.00003006046,0.0002657015,0.000008335913,0.0002123089,0.0001829977,0.000008428345,0.003294354,0.001787784,0.004109249,0.0009653917,0.009105701,0.9800297],"study_design_scores_gemma":[0.0004293066,0.0002101749,0.0000248778,0.0001624116,0.00002869459,0.00001493916,0.00003181453,0.9422507,0.03949583,0.0004750575,0.01656165,0.0003144843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007499744,0.00006531418,0.9905427,0.001251577,0.004327135,0.0008662753,0.00004103045,0.001807944,0.00034803],"genre_scores_gemma":[0.5376499,0.0000596795,0.4611106,0.0001117079,0.0002942691,0.0004675798,0.00001198007,0.00003020508,0.0002641508],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9797152,"threshold_uncertainty_score":0.9696618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03291627563722623,"score_gpt":0.2738679133519815,"score_spread":0.2409516377147553,"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."}}