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

Despite recommender systems play a key role in network content platforms,mining the user's interests is still a significant challenge. Existing workspredict the user interest by utilizing user behaviors, i.e., clicks, views,etc., but current solutions are ineffective when users perform unsettledactivities. The latter ones involve new users, which have few activities of anykind, and sparse users who have low-frequency behaviors. We uniformly describeboth these user-types as "cold users", which are very common but oftenneglected in network content platforms. To address this issue, we enhance therepresentation of the user interest by combining his social interest, e.g.,friendship, following bloggers, interest groups, etc., with the activitybehaviors. Thus, in this work, we present a novel algorithm entitled SocialNet,which adopts a two-stage method to progressively extract the coarse-grained andfine-grained social interest. Our technique then concatenates SocialNet'soutput with the original user representation to get the final userrepresentation that combines behavior interests and social interests. Offlineexperiments on Tencent video's recommender system demonstrate the superiorityover the baseline behavior-based model. The online experiment also shows asignificant performance improvement in clicks and view time in the real-worldrecommendation system. The source code is available athttps://github.com/Social4Rec/SocialNet.

Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent