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

Matrix completion is widely used in machine learning, engineering control,image processing, and recommendation systems. Currently, a popular algorithmfor matrix completion is Singular Value Threshold (SVT). In this algorithm, thesingular value threshold should be set first. However, in a recommendationsystem, the dimension of the preference matrix keeps changing. Therefore, it isdifficult to directly apply SVT. In addition, what the users of arecommendation system need is a sequence of personalized recommended resultsrather than the estimation of their scores. According to the above ideas, thispaper proposes a novel approach named probability completion model~(PCM). Byreducing the data dimension, the transitivity of the similar matrix, andsingular value decomposition, this approach quickly obtains a completion matrixwith the same probability distribution as the original matrix. The approachgreatly reduces the computation time based on the accuracy of the sacrificepart, and can quickly obtain a low-rank similarity matrix with data trendapproximation properties. The experimental results show that PCM can quicklygenerate a complementary matrix with similar data trends as the originalmatrix. The LCS score and efficiency of PCM are both higher than SVT.

A Fast Matrix-Completion-Based Approach for Recommendation Systems