{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00107957,0.0001605182,0.0002119172,0.0003335958,0.0002313703,0.0001634867,0.002343407,0.00003258412,0.0001552936],"category_scores_gemma":[0.0001422377,0.0001388063,0.0001530435,0.0003594949,0.00003271948,0.001011186,0.001431944,0.0001414701,0.000009986852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00023585,"about_ca_system_score_gemma":0.00003782143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009234143,"about_ca_topic_score_gemma":0.000004635418,"domain_scores_codex":[0.9983463,0.00001876477,0.0007831377,0.0002148164,0.0004637899,0.0001731602],"domain_scores_gemma":[0.9976667,0.00006094439,0.001142784,0.0002852834,0.0008120347,0.0000322157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006385223,0.00008521386,0.0005793349,0.0002023712,0.00008386438,1.07871e-8,0.001862348,0.00001216126,0.000254074,0.9510192,0.02281217,0.02302539],"study_design_scores_gemma":[0.001993789,0.0002212749,0.001386338,0.0002560453,0.00003247483,0.000010302,0.003744431,0.02095072,0.001974474,0.0470829,0.9219291,0.0004182047],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01764186,0.00003546052,0.266321,0.01820171,0.002934221,0.003652956,0.000217152,0.0003249703,0.6906707],"genre_scores_gemma":[0.9571369,0.00003162539,0.03821838,0.0004135283,0.00005688912,0.0008279202,0.00009026201,0.00001176211,0.003212788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.939495,"threshold_uncertainty_score":0.5660357,"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."}}