{"id":"W7125104546","doi":"10.1109/cbase67452.2025.11335564","title":"Improving Sequential Recommendations with TokenLevel LLM Representatio","year":2025,"lang":"","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Regent College","funders":"","keywords":"Initialization; Encoding (memory); Language model; Key (lock); Context (archaeology); Training set; Semantics (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":[0.001446623,0.001166805,0.001586455,0.001193146,0.000610136,0.001199328,0.001994099,0.001366609,0.003693871],"category_scores_gemma":[0.009305282,0.000817257,0.001029629,0.001523532,0.0005903263,0.003947776,0.001605675,0.001986203,0.002972411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057885,"about_ca_system_score_gemma":0.00158099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01149522,"about_ca_topic_score_gemma":0.02140395,"domain_scores_codex":[0.9988945,0.0003146581,0.0001050044,0.0003177324,0.0002644012,0.0001036984],"domain_scores_gemma":[0.9964846,0.001630196,0.0002524843,0.0009070261,0.0006030382,0.0001227154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006339361,0.0003655892,0.004366398,0.0002665245,0.000225404,0.00022353,0.0003443148,0.3157474,0.01265817,0.01077607,0.009060883,0.6453316],"study_design_scores_gemma":[0.00002038673,0.00006526951,0.0001857293,0.000009924484,0.00001855967,0.00004581178,0.00002289852,0.9904428,0.002714398,0.005252236,0.00120657,0.000015379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03098927,0.000413213,0.9619139,0.0002085919,0.0001016239,0.00007455869,0.0003152965,0.005009406,0.0009739995],"genre_scores_gemma":[0.4590468,0.0002977512,0.5316561,0.0003288463,0.00009128204,0.0002648894,0.001642807,0.0004615127,0.006210004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01149522,"threshold_uncertainty_score":0.02285665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03202739726353106,"score_gpt":0.3107249185881084,"score_spread":0.2786975213245773,"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."}}