Database system support for personalized recommendation applications

Mohamed Sarwat, Raha Moraffah, Mohamed F. Mokbel, James L. Avery

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


Personalized recommendation has become popular in modern web services. For instance, Amazon recommends new items to shoppers. Also, Netflix recommends shows to viewers, and Facebook recommends friends to its users. Despite the ubiquity of recommendation applications, classic database management systems still do not provide in-house support for recommending data stored in the database. In this paper, we present the anatomy of RecDB an open source PostgreSQLbased system that provides a unified approach for declarative data recommendation inside the database engine. RecDB realizes the personalized recommendation functionality as query operators inside the database kernel. That facilitates applying the recommendation functionality and typical database operations (e.g., Selection, Join, Top-k) side-by-side. To further reduce the application latency, RecDB pre-computes and caches the generated recommendation in the database. In the paper, we present extensive experiments that study the performance of personalized recommendation applications based on an actual implementation inside PostgreSQL 9.2 using real Movie recommendation and location-Aware recommendation scenarios. The results show that a recommendation-Aware database engine, i.e., RecDB, outperforms the classic approach that implements the recommendation logic on-Top of the database engine in various recommendation applications.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Number of pages12
ISBN (Electronic)9781509065431
StatePublished - May 16 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: Apr 19 2017Apr 22 2017

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Other33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/TerritoryUnited States
CitySan Diego

Bibliographical note

Funding Information:
Dr. Sarwat' s research is supported by the National Science Foundation under Grant IIS 1654861. Dr. Mokbel' s research is supported by NSF grants IIS-0952977, IIS-1218168, IIS-1525953, CNS-1512877.

Publisher Copyright:
© 2017 IEEE.


  • Analytics
  • Database
  • Indexing
  • Join
  • Machine learning
  • Personalization
  • Recommendation


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