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

Building recommendation algorithms is one of the most challenging tasks inMachine Learning. Although most of the recommendation systems are built onexplicit feedback available from the users in terms of rating or text, amajority of the applications do not receive such feedback. Here we consider therecommendation task where the only available data is the records of user-iteminteraction over web applications over time, in terms of subscription orpurchase of items; this is known as implicit feedback recommendation. There isusually a massive amount of such user-item interaction available for any webapplications. Algorithms like PLSI or Matrix Factorization runs severaliterations through the dataset, and may prove very expensive for largedatasets. Here we propose a recommendation algorithm based on Method of Moment,which involves factorization of second and third order moments of the dataset.Our algorithm can be proven to be globally convergent using PAC learningtheory. Further, we show how to extract the parameters using only three passesthrough the entire dataset. This results in a highly scalable algorithm thatscales up to million of users even on a machine with a single-core processorand 8 GB RAM and produces competitive performance in comparison with existingalgorithms.

Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees