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RecDB in action: recommendation made easy in relational databases

Authors:

Mohamed Sarwat,

James Avery,

Mohamed F. MokbelAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 6, Issue 12

Pages 1242 - 1245

https://doi.org/10.14778/2536274.2536286

Published: 01 August 2013 Publication History

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Abstract

In this paper, we demonstrate RecDB; a full-fledged database system that provides personalized recommendation to users. We implemented RecDB using an existing open source database system PostgreSQL, and we demonstrate the effectiveness of RecDB using two existing recommendation applications (1) Restaurant Recommendation, (2) Movie Recommendation. To make the demo even more interactive, we showcase a novel application that recommends research papers presented at VLDB 2013 to the conference attendees based on their publication history in DBLP.

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Cited By

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Adomavicius GBauman KTuzhilin AUnger M(2021)Context-Aware Recommender Systems: From Foundations to Recent DevelopmentsRecommender Systems Handbook10.1007/978-1-0716-2197-4_6(211-250)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_6
Park SLee SKim J(2020)Estimating Revenues of Seoul Commercial Alley Services using Tensor Decomposition & Generating Recommendation System2020 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp48618.2020.00-59(287-294)Online publication date: Mar-2020
https://doi.org/10.1109/BigComp48618.2020.00-59
Schelter SCelebi UDunning TMaltzahn CMalik T(2019)Efficient Incremental Cooccurrence Analysis for Item-Based Collaborative FilteringProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335784(61-72)Online publication date: 23-Jul-2019
https://dl.acm.org/doi/10.1145/3335783.3335784
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RecDB in action: recommendation made easy in relational databases
1. Information systems
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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 6, Issue 12

August 2013

264 pages

ISSN:2150-8097

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VLDB Endowment

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Published: 01 August 2013

Published in PVLDB Volume 6, Issue 12

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Adomavicius GBauman KTuzhilin AUnger M(2021)Context-Aware Recommender Systems: From Foundations to Recent DevelopmentsRecommender Systems Handbook10.1007/978-1-0716-2197-4_6(211-250)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_6
Park SLee SKim J(2020)Estimating Revenues of Seoul Commercial Alley Services using Tensor Decomposition & Generating Recommendation System2020 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp48618.2020.00-59(287-294)Online publication date: Mar-2020
https://doi.org/10.1109/BigComp48618.2020.00-59
Schelter SCelebi UDunning TMaltzahn CMalik T(2019)Efficient Incremental Cooccurrence Analysis for Item-Based Collaborative FilteringProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335784(61-72)Online publication date: 23-Jul-2019
https://dl.acm.org/doi/10.1145/3335783.3335784
Magdy AAbdelhafeez LKang YOng EMokbel M(2019)Microblogs data management: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00569-629:1(177-216)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1007/s00778-019-00569-6
Kitazawa TYui MPera SEkstrand MAmatriain XO'Donovan J(2018)Query-based simple and scalable recommender systems with apache hivemallProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3241592(502-503)Online publication date: 27-Sep-2018
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Li X(2018)Study on information recommendation of scientific and technological achievements based on user behavior modeling and big data mining2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)10.1109/ICCCBDA.2018.8386517(228-232)Online publication date: Apr-2018
https://doi.org/10.1109/ICCCBDA.2018.8386517
Margaris DVassilakis CGeorgiadis P(2018)Query personalization using social network information and collaborative filtering techniquesFuture Generation Computer Systems10.1016/j.future.2017.03.01578(440-450)Online publication date: Jan-2018
https://doi.org/10.1016/j.future.2017.03.015
Sarwat MMokbel M(2018)Collaborative FilteringEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_80733(509-513)Online publication date: 7-Dec-2018
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Sarwat MMokbel M(2018)Recommender SystemsEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_80732(3124-3128)Online publication date: 7-Dec-2018
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Aly AHammad MAhmed AChoudhary AWu KDong B(2017)SHRecProceedings of the 29th International Conference on Scientific and Statistical Database Management10.1145/3085504.3085523(1-11)Online publication date: 27-Jun-2017
https://dl.acm.org/doi/10.1145/3085504.3085523
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RecDB: towards DBMS support for online recommender systems

Adaptive recommendation for preferred information and browsing action based on web-browsing behavior

Action prediction models for recommender systems based on collaborative filtering and sequence mining hybridization

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