Paper Citation Record · LEDGER

Unifying Generative and Dense Retrieval for Sequential Recommendation

As of 15 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 20 inbound Pith citation observations for arXiv:2411.18814.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.18814 v2

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:56:06.875869Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:00:26.972723Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T19:00:06.171996Z

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy6
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7f0d2b95-c078-492c-84f2-fb1dabfeeaed · outbound

This paper cites an unresolved cited work.

Unifying Generative and Dense Retrieval for Sequential Recommendation Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:56:07.028305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1abae7c9-dca9-4568-829c-3be642ef0e74 · outbound

This paper cites an unresolved cited work.

Unifying Generative and Dense Retrieval for Sequential Recommendation Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:56:07.017354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c579753c-b31e-42ab-a902-5fe73c94dfbd · outbound

This paper cites an unresolved cited work.

Unifying Generative and Dense Retrieval for Sequential Recommendation Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:56:07.007035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d66def0f-295f-4eed-b014-288bec168b44 · outbound

This paper cites B.3 Data Statistics In Table 3, we present the statistics of the datasets used in our evaluation.

Unifying Generative and Dense Retrieval for Sequential Recommendation B.3 Data Statistics In Table 3, we present the statistics of the datasets used in our evaluation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:56:06.995859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3980f300-fb43-48c3-b136-881619b3d9ad · outbound

This paper cites A bidirectional Transformer-based model that encodes item information using key-value attributes described by text.

Unifying Generative and Dense Retrieval for Sequential Recommendation A bidirectional Transformer-based model that encodes item information using key-value attributes described by text

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:56:06.942280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c7029185-0a90-45ae-a887-db9d497ca17b · outbound

This paper cites A self-attention based sequential recommendation model that learns to predict the next item ID based on the user’s interaction history.

Unifying Generative and Dense Retrieval for Sequential Recommendation A self-attention based sequential recommendation model that learns to predict the next item ID based on the user’s interaction history

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:56:06.985333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 876e66ad-b302-497f-add5-53c0ff2cd92a · outbound

This paper cites This method extends SASRec by incorporating item features into the self-attention model, allowing it to leverage prior information about cold-start items through their attributes.

Unifying Generative and Dense Retrieval for Sequential Recommendation This method extends SASRec by incorporating item features into the self-attention model, allowing it to leverage prior information about cold-start items through their attributes

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:56:06.975199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 83a14e3e-cfbc-4138-b7b7-da6e3a1c781f · outbound

This paper cites A self-attention based model that utilizes data correlation to create self-supervision signals, improving sequential recommendation through pre-training.

Unifying Generative and Dense Retrieval for Sequential Recommendation A self-attention based model that utilizes data correlation to create self-supervision signals, improving sequential recommendation through pre-training

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:56:06.963835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a73a1680-d368-4113-a377-07baa78463c7 · outbound

This paper cites an unresolved cited work.

Unifying Generative and Dense Retrieval for Sequential Recommendation Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:56:06.953524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a934bf90-b190-4a8c-a927-51bae4ea232d · outbound

This paper cites an unresolved cited work.

Unifying Generative and Dense Retrieval for Sequential Recommendation Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-12T10:56:06.931567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d1e7db82-2d2a-4084-9248-4e8c9d72be8b · outbound

This paper cites (b) The next item’s text representation through the embedding head.

Unifying Generative and Dense Retrieval for Sequential Recommendation (b) The next item’s text representation through the embedding head

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:56:06.920121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1e51b226-f748-4900-ae30-511ac0a80da6 · outbound

This paper cites Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models.

Unifying Generative and Dense Retrieval for Sequential Recommendation Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T10:56:06.834598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 71704dfc-97fb-4156-984e-7f024dac8f4b · inbound

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders cites this paper.

Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:26.972723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30c4e3cb-512e-4d7e-9802-c7ac3a931207 · inbound

Generating Long Semantic IDs in Parallel for Recommendation cites this paper.

Generating Long Semantic IDs in Parallel for Recommendation Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T10:21:54.276270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eac8234f-e163-4fb6-973a-70287d790008 · inbound

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models cites this paper.

GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:06.952470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8953740-6668-48b8-8ec8-f9618b214890 · inbound

Generative Recommendation with Semantic IDs: A Practitioner's Handbook cites this paper.

Generative Recommendation with Semantic IDs: A Practitioner's Handbook Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T12:01:13.384482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71ab3836-dd78-4f72-81b6-293ba4099882 · inbound

Sequential Data Augmentation for Generative Recommendation cites this paper.

Sequential Data Augmentation for Generative Recommendation Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:44:24.362467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 214e1b4e-2a85-4abd-a8a9-17612208af5b · inbound

FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets cites this paper.

FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T15:17:07.626367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62bc7a19-aa42-4dcd-9045-0462e3f7cf28 · inbound

GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items cites this paper.

GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:55:33.413631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a0024a4f-5d05-4962-a158-fba8adb4ee43 · inbound

Semantic IDs for Recommender Systems at Snapchat: Use Cases, Technical Challenges, and Design Choices cites this paper.

Semantic IDs for Recommender Systems at Snapchat: Use Cases, Technical Challenges, and Design Choices Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-13T11:47:58.173367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b53f95e-ac9e-4024-adda-98ea499cf1ff · inbound

Mitigating Collaborative Semantic ID Staleness in Generative Retrieval cites this paper.

Mitigating Collaborative Semantic ID Staleness in Generative Retrieval Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:00:29.465419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 582070c6-97c7-43f9-bb2b-1c12abb91bcb · inbound

MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches cites this paper.

MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:16:07.688605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 76ea22f0-cb55-4c29-a0cb-74a8e458975b · inbound

CapsID: Soft-Routed Variable-Length Semantic IDs for Generative Recommendation cites this paper.

CapsID: Soft-Routed Variable-Length Semantic IDs for Generative Recommendation Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:26:12.829000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 44dab3d1-6ecd-4ed8-96a8-fabe33193c50 · inbound

Expressiveness Limits of Autoregressive Semantic ID Generation in Generative Recommendation cites this paper.

Expressiveness Limits of Autoregressive Semantic ID Generation in Generative Recommendation Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:21:12.643115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2b6307b7-78af-420d-be0d-f9e34668f013 · inbound

Bridging Textual Profiles and Latent User Embeddings for Personalization cites this paper.

Bridging Textual Profiles and Latent User Embeddings for Personalization Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:50:56.736262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9938809f-5381-4834-a9bc-8080031abcbb · inbound

Conditional Memory Enhanced Item Representation for Generative Recommendation cites this paper.

Conditional Memory Enhanced Item Representation for Generative Recommendation Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:32:06.297649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 70d84121-745e-46f6-af35-c21c118f3b5c · inbound

MLPs are Efficient Distilled Generative Recommenders cites this paper.

MLPs are Efficient Distilled Generative Recommenders Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:19:28.230894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5e7c6a1b-8f16-4497-944f-6bd411de6df7 · inbound

LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation cites this paper.

LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-21T01:33:56.572879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ce614079-b44f-4054-80da-a40b49e5a7d6 · inbound

UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale cites this paper.

UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:32:37.231417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 56688349-c8f9-4abc-8dda-4e51b359fcbd · inbound

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems cites this paper.

TokenMinds: Pretrained User Tokens and Embeddings for User Understanding in Large Recommender Systems Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:00:06.175113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d7fd06b4-ea2e-40e0-9df8-0b7ad80f8b2f · inbound

Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs cites this paper.

Beyond Item Order: Temporal Gap Tokenization for Generative Recommendation with Semantic IDs Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-11T23:02:18.007239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54098a08-8e93-4666-a14a-070f805550d9 · inbound

From Understanding to Action: Feedback-Grounded Policy Discovery for Generative Recommendation cites this paper.

From Understanding to Action: Feedback-Grounded Policy Discovery for Generative Recommendation Unifying Generative and Dense Retrieval for Sequential Recommendation

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-01T01:28:22.073426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.