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/

Zendy Plus

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

Zendy Tools

Zendy Open

It is well known that collaborative filtering (CF) based recommender systemsprovide better modeling of users and items associated with considerable ratinghistory. The lack of historical ratings results in the user and the itemcold-start problems. The latter is the main focus of this work. Most of thecurrent literature addresses this problem by integrating content-basedrecommendation techniques to model the new item. However, in many cases suchcontent is not available, and the question arises is whether this problem canbe mitigated using CF techniques only. We formalize this problem as anoptimization problem: given a new item, a pool of available users, and a budgetconstraint, select which users to assign with the task of rating the new itemin order to minimize the prediction error of our model. We show that theobjective function is monotone-supermodular, and propose efficient optimaldesign based algorithms that attain an approximation to its optimum. Ourfindings are verified by an empirical study using the Netflix dataset, wherethe proposed algorithms outperform several baselines for the problem at hand.

Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design