{"id":"W4296559601","doi":"10.1145/3511808.3557229","title":"Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); McGill University","funders":"","keywords":"Computer science; Pairwise comparison; Matrix decomposition; Recommender system; Machine learning; Graph; Precision and recall; Ranking (information retrieval); Information retrieval; Artificial intelligence; Theoretical computer science","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":[0.002288854,0.00122973,0.002213411,0.001199167,0.0004826493,0.0007363327,0.00225224,0.001614905,0.002278476],"category_scores_gemma":[0.0105369,0.0007069501,0.0009569834,0.001487884,0.0007311051,0.002807656,0.001094041,0.002156633,0.0008396771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001075888,"about_ca_system_score_gemma":0.00118244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00854905,"about_ca_topic_score_gemma":0.01377618,"domain_scores_codex":[0.9984969,0.0004351362,0.00008249732,0.0003829617,0.0004427894,0.0001596963],"domain_scores_gemma":[0.9959071,0.002190323,0.0002914734,0.0005436313,0.0008874178,0.0001800818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000626745,0.0006982171,0.006066887,0.000303296,0.0001505464,0.0001553273,0.0002480411,0.4842679,0.009180482,0.009174073,0.006576398,0.4825521],"study_design_scores_gemma":[0.00001830344,0.00006736417,0.0003026885,0.000007791412,0.00001302984,0.00002502592,0.000006299595,0.9960719,0.0006788293,0.002477908,0.000321557,0.000009297979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03350596,0.0005708359,0.9629175,0.0001574396,0.00009532622,0.0001106678,0.0001304752,0.001096381,0.001415398],"genre_scores_gemma":[0.7882604,0.0003836028,0.2066058,0.0003132976,0.0002032251,0.0002189236,0.0006570317,0.0001404682,0.003217187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00854905,"threshold_uncertainty_score":0.01699859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0763719245103032,"score_gpt":0.3153775664848288,"score_spread":0.2390056419745256,"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."}}