{"paper":{"title":"Echoes in Filter Bubble: Diagnosing and Curing Popularity Bias in Generative Recommenders","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Generative recommenders develop severe popularity bias from token-level optimization flaws and uniform item tokenization, which Ghost corrects using asymmetric unlikelihood optimization and skeleton-founded tokenization.","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Bangguo Zhu, Chengqi Zhang, Hao Chen, Jun Yin, Peng Huo, Ruochen Liu, Senzhang Wang, Shirui Pan","submitted_at":"2026-05-16T06:02:40Z","abstract_excerpt":"Recently, Generative Recommenders (GRs), characterized by a unified end-to-end framework, have exhibited astonishing potential in transforming the recommendation paradigm. Despite their effectiveness, we recognize that GRs are still susceptible to the long-standing issue of popularity bias that has pervaded the recommendation community. Although a few studies have attempted to extend traditional debiasing methods to GRs, their effectiveness is marginal, and the fundamental reason why GRs suffer from popularity bias remains under-explored. To bridge this gap, this study focuses on two core aspe"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The severe popularity bias emerges from the confluence of a token-level optimization flaw and the undifferentiated property of item tokenization. Ghost substantially alleviates popularity bias and promotes fairer recommendations, while incurring slight degradation to the overall recommendation utility.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The theoretical analyses correctly pinpoint the root causes of popularity bias in generative recommenders, and the proposed asymmetric unlikelihood optimization together with skeleton-founded tokenization effectively mitigate these causes without introducing new biases or requiring extensive post-hoc adjustments.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Ghost reduces popularity bias in generative recommenders through asymmetric unlikelihood optimization and skeleton-founded tokenization.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Generative recommenders develop severe popularity bias from token-level optimization flaws and uniform item tokenization, which Ghost corrects using asymmetric unlikelihood optimization and skeleton-founded tokenization.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"66956e986f9c986dd9ee3be94a944d113b9419a1da4cd5d3a51d881780f6559d"},"source":{"id":"2605.16825","kind":"arxiv","version":1},"verdict":{"id":"39f21910-0740-4853-9eb4-408e686847ef","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:36:58.612047Z","strongest_claim":"The severe popularity bias emerges from the confluence of a token-level optimization flaw and the undifferentiated property of item tokenization. Ghost substantially alleviates popularity bias and promotes fairer recommendations, while incurring slight degradation to the overall recommendation utility.","one_line_summary":"Ghost reduces popularity bias in generative recommenders through asymmetric unlikelihood optimization and skeleton-founded tokenization.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The theoretical analyses correctly pinpoint the root causes of popularity bias in generative recommenders, and the proposed asymmetric unlikelihood optimization together with skeleton-founded tokenization effectively mitigate these causes without introducing new biases or requiring extensive post-hoc adjustments.","pith_extraction_headline":"Generative recommenders develop severe popularity bias from token-level optimization flaws and uniform item tokenization, which Ghost corrects using asymmetric unlikelihood optimization and skeleton-founded tokenization."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16825/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.263145Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:51:12.772002Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.264014Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.406900Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"30ac0aa8453119816cc18cb187c9450c2bebf1ea2e4564b98c5d3180fa438d48"},"references":{"count":48,"sample":[{"doi":"","year":2018,"title":"Deep interest network for click-through rate prediction,","work_id":"fff87a5a-8e67-4b88-9795-93e8ab885c85","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"Deep neural networks for youtube recommendations,","work_id":"b6151051-3638-48e9-b0e7-d852e0354625","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Deepinf: Social influence prediction with deep learning,","work_id":"ef73f8e6-b611-4cbc-9201-45955f44c50d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Recommender systems with generative retrieval,","work_id":"5e716d32-7499-4ef1-9f7a-73dd32168954","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Adapt- ing large language models by integrating collaborative semantics for recommendation,","work_id":"e3ebcb6e-47f0-4c9c-9599-6bb481c16ada","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":48,"snapshot_sha256":"7075b540928e313d569e03c7127422089969394e16183963f3d154ce5e1c7504","internal_anchors":2},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}