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

A collection of datasets crawled from Amazon, “Amazon reviews”, is popular in the evaluation of recommendation systems. These datasets, however, contain redundancies (duplicated recommendations for variants of certain items). These redundancies went unnoticed in earlier use of these datasets and thus incurred to a certain extent wrong conclusions in the evaluation of algorithms tested on these datasets. We analyze the nature and amount of these redundancies and their impact on the evaluation of recommendation methods. While the general and obvious conclusion is that redundancies should be avoided and datasets should be carefully preprocessed, we observe more specifically that their impact depends on the complexity of the methods. With this work, we also want to raise the awareness of the importance of data quality, model understanding, and appropriate evaluation.

Redundancies in Data and their Effect on the Evaluation of Recommendation Systems: A Case Study on the Amazon Reviews Datasets