English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recent studies have proposed unified user modeling frameworks that leverageuser behavior data from various applications. Many of them benefit fromutilizing users' behavior sequences as plain texts, representing richinformation in any domain or system without losing generality. Hence, aquestion arises: Can language modeling for user history corpus help improverecommender systems? While its versatile usability has been widely investigatedin many domains, its applications to recommender systems still remainunderexplored. We show that language modeling applied directly to task-specificuser histories achieves excellent results on diverse recommendation tasks.Also, leveraging additional task-agnostic user histories delivers significantperformance benefits. We further demonstrate that our approach can providepromising transfer learning capabilities for a broad spectrum of real-worldrecommender systems, even on unseen domains and services.

Pivotal Role of Language Modeling in Recommender Systems: Enriching Task-specific and Task-agnostic Representation Learning