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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Conversational Recommender Systems (CRSs) in E-commerce platforms aim torecommend items to users via multiple conversational interactions.Click-through rate (CTR) prediction models are commonly used for rankingcandidate items. However, most CRSs are suffer from the problem of datascarcity and sparseness. To address this issue, we propose a novelknowledge-enhanced deep cross network (K-DCN), a two-step (pretrain andfine-tune) CTR prediction model to recommend items. We first construct abillion-scale conversation knowledge graph (CKG) from information about users,items and conversations, and then pretrain CKG by introducing knowledge graphembedding method and graph convolution network to encode semantic andstructural information respectively.To make the CTR prediction model sensibleof current state of users and the relationship between dialogues and items, weintroduce user-state and dialogue-interaction representations based onpre-trained CKG and propose K-DCN.In K-DCN, we fuse the user-staterepresentation, dialogue-interaction representation and other normal featurerepresentations via deep cross network, which will give the rank of candidateitems to be recommended.We experimentally prove that our proposal significantlyoutperforms baselines and show it's real application in Alime.

Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph