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Hllm Paper 1

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-05-02.)

Hllm Paper 1 has 14 facts recorded in Dontopedia across 1 reference, with 1 live disagreement.

14 facts·11 predicates·1 sources·1 in dispute

Mostly:has authors(4), achieves performance(1), has arxiv link(1)

Maturity scale raw canonical shape-checked rule-derived certified

Has Authorsin disputehasAuthors

Achieves PerformanceachievesPerformance

  • state-of-the-art performance and scalability[1]all time · Part 434

Has SummaryhasSummary

  • Proposes a hierarchical LLM architecture for sequential recommendation systems with item and user modeling, achieving state-of-the-art performance and scalability.[1]all time · Part 434

Has TitlehasTitle

  • HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling[1]all time · Part 434

Includes ModelingincludesModeling

  • item and user modeling[1]all time · Part 434

Is Academic PaperisAcademicPaper

  • true[1]all time · Part 434

Proposes ArchitectureproposesArchitecture

  • hierarchical LLM architecture[1]all time · Part 434

Published onpublishedOn

  • 2024-09-19[1]all time · Part 434

Targets DomaintargetsDomain

  • sequential recommendation systems[1]all time · Part 434

Claims SuperiorityclaimsSuperiority

  • state-of-the-art[1]all time · Part 434

Inbound mentions (1)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

listedPapersListed Papers(1)

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

achievesPerformanceblah/omega/part-434
state-of-the-art performance and scalability
claimsSuperiorityblah/omega/part-434
state-of-the-art
hasArxivLinkblah/omega/part-434
http://arxiv.org/abs/2409.12740v1
hasAuthorsblah/omega/part-434
ex:bingyue-peng
hasAuthorsblah/omega/part-434
ex:junyi-chen
hasAuthorsblah/omega/part-434
ex:lu-chi
hasAuthorsblah/omega/part-434
ex:zehuan-yuan
hasSummaryblah/omega/part-434
Proposes a hierarchical LLM architecture for sequential recommendation systems with item and user modeling, achieving state-of-the-art performance and scalability.
hasTitleblah/omega/part-434
HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling
includesModelingblah/omega/part-434
item and user modeling
isAcademicPaperblah/omega/part-434
true
proposesArchitectureblah/omega/part-434
hierarchical LLM architecture
publishedOnblah/omega/part-434
2024-09-19
targetsDomainblah/omega/part-434
sequential recommendation systems

References (1)

1 references
  1. [1]Part 43414 facts
    customctx:discord/blah/omega/part-434

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