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

Count data are often used in recommender systems: they are widespread (songplay counts, product purchases, clicks on web pages) and can reveal userpreference without any explicit rating from the user. Such data are known to besparse, over-dispersed and bursty, which makes their direct use in recommendersystems challenging, often leading to pre-processing steps such asbinarization. The aim of this paper is to build recommender systems from theseraw data, by means of the recently proposed compound Poisson Factorization(cPF). The paper contributions are three-fold: we present a unified frameworkfor discrete data (dcPF), leading to an adaptive and scalable algorithm; weshow that our framework achieves a trade-off between Poisson Factorization (PF)applied to raw and binarized data; we study four specific instances that arerelevant to recommendation and exhibit new links with combinatorics.Experiments with three different datasets show that dcPF is able to effectivelyadjust to over-dispersion, leading to better recommendation scores whencompared with PF on either raw or binarized data.

Recommendation from Raw Data with Adaptive Compound Poisson Factorization