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

A vast amount of user behavior data is constantly accumulating on today'slarge recommendation platforms, recording users' various interests and tastes.Preserving knowledge from the old data while new data continually arrives is avital problem for recommender systems. Existing approaches generally seek tosave the knowledge implicitly in the model parameters. However, such aparameter-centric approach lacks scalability and flexibility -- the capacity ishard to scale, and the knowledge is inflexible to utilize. Hence, in this work,we propose a framework that turns massive user behavior data to retrievableknowledge (D2K). It is a data-centric approach that is model-agnostic and easyto scale up. Different from only storing unary knowledge such as the user-sideor item-side information, D2K propose to store ternary knowledge forrecommendation, which is determined by the complete recommendation factors --user, item, and context. The knowledge retrieved by target samples can bedirectly used to enhance the performance of any recommendation algorithms.Specifically, we introduce a Transformer-based knowledge encoder to transformthe old data into knowledge with the user-item-context cross features. Apersonalized knowledge adaptation unit is devised to effectively exploit theinformation from the knowledge base by adapting the retrieved knowledge to thetarget samples. Extensive experiments on two public datasets show that D2Ksignificantly outperforms existing baselines and is compatible with a majorcollection of recommendation algorithms.

D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems