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

Prior study has shown that pretrained language models (PLM) can boost theperformance of text-based recommendation. In contrast to previous works thateither use PLM to encode user history as a whole input text, or impose anadditional aggregation network to fuse multi-turn history representations, wepropose a unified local- and global-attention Transformer encoder to bettermodel two-level contexts of user history. Moreover, conditioned on user historyencoded by Transformer encoders, our framework leverages Transformer decodersto estimate the language perplexity of candidate text items, which can serve asa straightforward yet significant contrastive signal for user-item textmatching. Based on this, our framework, UniTRec, unifies the contrastiveobjectives of discriminative matching scores and candidate text perplexity tojointly enhance text-based recommendation. Extensive evaluation shows thatUniTRec delivers SOTA performance on three text-based recommendation tasks.Code is available at https://github.com/Veason-silverbullet/UniTRec.

UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation