Data quality matters in recommender systems

Oren Sar Shalom, Shlomo Berkovsky, Royi Ronen, Elad Ziklik, Amir Amihood

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

8 Citations (Scopus)


Although data quality has been recognized as an important factor in the broad information systems research, it has received little attention in recommender systems. Data quality matters are typically addressed in recommenders by ad-hoc cleansing methods, which prune noisy or unreliable records from the data. However, the setting of the cleansing parameters is often done arbitrarily, without thorough consideration of the data characteristics. In this work, we turn to two central data quality problems in recommender systems: sparsity and redundancy. We devise models for setting data-dependent thresholds and sampling levels, and evaluate these using a collection of public and proprietary datasets. We observe that the models accurately predict data cleansing parameters, while having minor effect on the accuracy of the generated recommendations.
Original languageEnglish
Title of host publicationProceedings of the 9th ACM Conference on Recommender Systems
Subtitle of host publicationRecSys '15
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages4
ISBN (Electronic)9781450336925
Publication statusPublished - 16 Sep 2015
Externally publishedYes
Event9th ACM Conference on Recommender Systems, RecSys 2015 - Vienna, Austria
Duration: 16 Sep 201520 Sep 2015


Conference9th ACM Conference on Recommender Systems, RecSys 2015


Dive into the research topics of 'Data quality matters in recommender systems'. Together they form a unique fingerprint.

Cite this