{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:QMLY3MWSA6JDWC4HICUH2YNS5T","short_pith_number":"pith:QMLY3MWS","schema_version":"1.0","canonical_sha256":"83178db2d207923b0b8740a87d61b2ecc27c3cf0cabbac3bd38caff0f386212f","source":{"kind":"arxiv","id":"2108.06479","version":1},"attestation_state":"computed","paper":{"title":"Contrastive Self-supervised Sequential Recommendation with Robust Augmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Caiming Xiong, Jia Li, Julian McAuley, Philip S. Yu, Yongjun Chen, Zhiwei Liu","submitted_at":"2021-08-14T07:15:25Z","abstract_excerpt":"Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approaches model transition probabilities between items in a sequence, whether through Markov chains, recurrent networks, or more recently, Transformers. However both old and new issues remain, including data-sparsity and noisy data; such issues can impair the performance, especially in complex, parameter-hungry models. In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequenti"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2108.06479","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2021-08-14T07:15:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0097145d2e74a4c79b6c376d38efc588a04daa5b8ccdbd79d05d6c26e873a2fc","abstract_canon_sha256":"eec528c689d299a571d7117eb1889cf955e5313108bc485dece2e0e5599fa326"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:05:54.970579Z","signature_b64":"WKJEna230uS/tsfL7sBazNC1GOWDOEPQNMxDPfzHMpagCxfkrLggjr/I0IK7Mz6IU07LKUyJFxWHCmtzipOeCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83178db2d207923b0b8740a87d61b2ecc27c3cf0cabbac3bd38caff0f386212f","last_reissued_at":"2026-07-05T03:05:54.970162Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:05:54.970162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contrastive Self-supervised Sequential Recommendation with Robust Augmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Caiming Xiong, Jia Li, Julian McAuley, Philip S. Yu, Yongjun Chen, Zhiwei Liu","submitted_at":"2021-08-14T07:15:25Z","abstract_excerpt":"Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approaches model transition probabilities between items in a sequence, whether through Markov chains, recurrent networks, or more recently, Transformers. However both old and new issues remain, including data-sparsity and noisy data; such issues can impair the performance, especially in complex, parameter-hungry models. In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequenti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.06479","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.06479/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2108.06479","created_at":"2026-07-05T03:05:54.970216+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.06479v1","created_at":"2026-07-05T03:05:54.970216+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.06479","created_at":"2026-07-05T03:05:54.970216+00:00"},{"alias_kind":"pith_short_12","alias_value":"QMLY3MWSA6JD","created_at":"2026-07-05T03:05:54.970216+00:00"},{"alias_kind":"pith_short_16","alias_value":"QMLY3MWSA6JDWC4H","created_at":"2026-07-05T03:05:54.970216+00:00"},{"alias_kind":"pith_short_8","alias_value":"QMLY3MWS","created_at":"2026-07-05T03:05:54.970216+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":15,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25415","citing_title":"S2-CAR: Segmentation-Supervised Complexity-Adaptive Recommendation","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2606.18897","citing_title":"SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2606.11023","citing_title":"Generative Archetype-Grounded Item Representations for Sequential Recommendation","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28493","citing_title":"Looking Farther with Confidence: Uncertainty-Guided Future Learning for Sequential Recommendation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2504.19178","citing_title":"Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2512.14047","citing_title":"AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02833","citing_title":"BIPCL: Bilateral Intent-Enhanced Sequential Recommendation via Embedding Perturbation Contrastive Learning","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04038","citing_title":"FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11707","citing_title":"Quality-Aware Collaborative Multi-Positive Contrastive Learning for Sequential Recommendation","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04723","citing_title":"Rethinking Convolutional Networks for Attribute-Aware Sequential Recommendation","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05379","citing_title":"Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05309","citing_title":"Pay Attention to Sequence Split: Uncovering the Impacts of Sub-Sequence Splitting on Sequential Recommendation Models","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14114","citing_title":"ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14581","citing_title":"Behavior-Aware Dual-Channel Preference Learning for Heterogeneous Sequential Recommendation","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16121","citing_title":"Beyond One-Size-Fits-All: Adaptive Test-Time Augmentation for Sequential Recommendation","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T","json":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T.json","graph_json":"https://pith.science/api/pith-number/QMLY3MWSA6JDWC4HICUH2YNS5T/graph.json","events_json":"https://pith.science/api/pith-number/QMLY3MWSA6JDWC4HICUH2YNS5T/events.json","paper":"https://pith.science/paper/QMLY3MWS"},"agent_actions":{"view_html":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T","download_json":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T.json","view_paper":"https://pith.science/paper/QMLY3MWS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.06479&json=true","fetch_graph":"https://pith.science/api/pith-number/QMLY3MWSA6JDWC4HICUH2YNS5T/graph.json","fetch_events":"https://pith.science/api/pith-number/QMLY3MWSA6JDWC4HICUH2YNS5T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T/action/storage_attestation","attest_author":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T/action/author_attestation","sign_citation":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T/action/citation_signature","submit_replication":"https://pith.science/pith/QMLY3MWSA6JDWC4HICUH2YNS5T/action/replication_record"}},"created_at":"2026-07-05T03:05:54.970216+00:00","updated_at":"2026-07-05T03:05:54.970216+00:00"}