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

Recommender Systems (RS) aim to provide personalized suggestions of items forusers against consumer over-choice. Although extensive research has beenconducted to address different aspects and challenges of RS, there still existsa gap between academic research and industrial applications. Specifically, mostof the existing models still work in an offline manner, in which therecommender is trained on a large static training set and evaluated on a veryrestrictive testing set in a one-time process. RS will stay unchanged until thenext batch retrain is performed. We frame such RS as Batch Update RecommenderSystems (BURS). In reality, they have to face the challenges where RS areexpected to be instantly updated with new data streaming in, and generateupdated recommendations for current user activities based on the newly arriveddata. We frame such RS as Incremental Update Recommender Systems (IURS).  In this article, we offer a systematic survey of incremental update forneural recommender systems. We begin the survey by introducing key concepts andformulating the task of IURS. We then illustrate the challenges in IURScompared with traditional BURS. Afterwards, we detail the introduction ofexisting literature and evaluation issues. We conclude the survey by outliningsome prominent open research issues in this area.

A Survey on Incremental Update for Neural Recommender Systems