RecDB: Towards DBMS support for online recommender systems

Mohamed Elsayed, Mohamed F. Mokbel

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

3 Citations (Scopus)

Abstract

Recommender systems have become popular in both commercial and academic settings. The main purpose of recommender systems is to suggest to users useful and interesting items or content (data) from a considerably large set of items. Traditional recommender systems do not take into account system issues (i.e., scalability and query efficiency). In an age of staggering web use growth and everpopular social media applications (e.g., Facebook, Google Reader), users are expressing their opinions over a diverse set of data (e.g., news stories, Facebook posts, retail purchases) faster than ever. In this paper, we propose RecDB; a fully fledged database system that provides online recommendation to users. We implement RecDB using existing open source database system Apache Derby, and we use showcase the effectiveness of RecDB by adopting inside Sindbad; a Location-Based Social Networking system developed at University of Minnesota.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data
Pages33-37
Number of pages5
DOIs
StatePublished - 2012
Externally publishedYes
EventSIGMOD/PODS '12 PhD Symposium - Scottsdale, AZ, United States
Duration: May 20 2012May 20 2012

Other

OtherSIGMOD/PODS '12 PhD Symposium
CountryUnited States
CityScottsdale, AZ
Period5/20/125/20/12

Fingerprint

Recommender systems
Scalability

Keywords

  • filtered recommendation
  • model maintenance
  • query processing
  • recommender systems
  • social networking

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Elsayed, M., & Mokbel, M. F. (2012). RecDB: Towards DBMS support for online recommender systems. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 33-37) https://doi.org/10.1145/2213598.2213608

RecDB : Towards DBMS support for online recommender systems. / Elsayed, Mohamed; Mokbel, Mohamed F.

Proceedings of the ACM SIGMOD International Conference on Management of Data. 2012. p. 33-37.

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

Elsayed, M & Mokbel, MF 2012, RecDB: Towards DBMS support for online recommender systems. in Proceedings of the ACM SIGMOD International Conference on Management of Data. pp. 33-37, SIGMOD/PODS '12 PhD Symposium, Scottsdale, AZ, United States, 5/20/12. https://doi.org/10.1145/2213598.2213608
Elsayed M, Mokbel MF. RecDB: Towards DBMS support for online recommender systems. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 2012. p. 33-37 https://doi.org/10.1145/2213598.2213608
Elsayed, Mohamed ; Mokbel, Mohamed F. / RecDB : Towards DBMS support for online recommender systems. Proceedings of the ACM SIGMOD International Conference on Management of Data. 2012. pp. 33-37
@inproceedings{7d565279a8a14ebfa227cf9580358adb,
title = "RecDB: Towards DBMS support for online recommender systems",
abstract = "Recommender systems have become popular in both commercial and academic settings. The main purpose of recommender systems is to suggest to users useful and interesting items or content (data) from a considerably large set of items. Traditional recommender systems do not take into account system issues (i.e., scalability and query efficiency). In an age of staggering web use growth and everpopular social media applications (e.g., Facebook, Google Reader), users are expressing their opinions over a diverse set of data (e.g., news stories, Facebook posts, retail purchases) faster than ever. In this paper, we propose RecDB; a fully fledged database system that provides online recommendation to users. We implement RecDB using existing open source database system Apache Derby, and we use showcase the effectiveness of RecDB by adopting inside Sindbad; a Location-Based Social Networking system developed at University of Minnesota.",
keywords = "filtered recommendation, model maintenance, query processing, recommender systems, social networking",
author = "Mohamed Elsayed and Mokbel, {Mohamed F.}",
year = "2012",
doi = "10.1145/2213598.2213608",
language = "English (US)",
isbn = "9781450313261",
pages = "33--37",
booktitle = "Proceedings of the ACM SIGMOD International Conference on Management of Data",

}

TY - GEN

T1 - RecDB

T2 - Towards DBMS support for online recommender systems

AU - Elsayed, Mohamed

AU - Mokbel, Mohamed F.

PY - 2012

Y1 - 2012

N2 - Recommender systems have become popular in both commercial and academic settings. The main purpose of recommender systems is to suggest to users useful and interesting items or content (data) from a considerably large set of items. Traditional recommender systems do not take into account system issues (i.e., scalability and query efficiency). In an age of staggering web use growth and everpopular social media applications (e.g., Facebook, Google Reader), users are expressing their opinions over a diverse set of data (e.g., news stories, Facebook posts, retail purchases) faster than ever. In this paper, we propose RecDB; a fully fledged database system that provides online recommendation to users. We implement RecDB using existing open source database system Apache Derby, and we use showcase the effectiveness of RecDB by adopting inside Sindbad; a Location-Based Social Networking system developed at University of Minnesota.

AB - Recommender systems have become popular in both commercial and academic settings. The main purpose of recommender systems is to suggest to users useful and interesting items or content (data) from a considerably large set of items. Traditional recommender systems do not take into account system issues (i.e., scalability and query efficiency). In an age of staggering web use growth and everpopular social media applications (e.g., Facebook, Google Reader), users are expressing their opinions over a diverse set of data (e.g., news stories, Facebook posts, retail purchases) faster than ever. In this paper, we propose RecDB; a fully fledged database system that provides online recommendation to users. We implement RecDB using existing open source database system Apache Derby, and we use showcase the effectiveness of RecDB by adopting inside Sindbad; a Location-Based Social Networking system developed at University of Minnesota.

KW - filtered recommendation

KW - model maintenance

KW - query processing

KW - recommender systems

KW - social networking

UR - http://www.scopus.com/inward/record.url?scp=84862612958&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862612958&partnerID=8YFLogxK

U2 - 10.1145/2213598.2213608

DO - 10.1145/2213598.2213608

M3 - Conference contribution

AN - SCOPUS:84862612958

SN - 9781450313261

SP - 33

EP - 37

BT - Proceedings of the ACM SIGMOD International Conference on Management of Data

ER -