research-article

Towards a scalable social recommender engine for online marketplaces: the case of apache solr

Authors:

Dominik Kowald,

Christoph TrattnerAuthors Info & Claims

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

Pages 817 - 822

https://doi.org/10.1145/2567948.2579245

Published: 07 April 2014 Publication History

Abstract

Recent research has unveiled the importance of online social networks for improving the quality of recommenders in several domains, what has encouraged the research community to investigate ways to better exploit the social information for recommendations. However, there is a lack of work that offers details of frameworks that allow an easy integration of social data with traditional recommendation algorithms in order to yield a straight-forward and scalable implementation of new and existing systems. Furthermore, it is rare to find details of performance evaluations of recommender systems such as hardware and software specifications or benchmarking results of server loading tests. In this paper we intend to bridge this gap by presenting the details of a social recommender engine for online marketplaces built upon the well-known search engine Apache Solr. We describe our architecture and also share implementation details to facilitate the re-use of our approach by people implementing recommender systems. In addition, we evaluate our framework from two perspectives: (a) recommendation algorithms and data sources, and (b) system performance under server stress tests. Using a dataset from the SecondLife virtual world that has both trading and social interactions, we contribute to research in social recommenders by showing how certain social features allow to improve recommendations in online marketplaces. On the platform implementation side, our evaluation results can serve as a baseline to people searching for performance references in terms of scalability, model training and testing trade-offs, real-time server performance and the impact of model updates in a production system.

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Cited By

S Hungund T(2024)Enhanced Spark Cluster Recommendation Engine Powered by Generative AIInternational Journal of Research in Science and Technology10.37648/ijrst.v14i01.00414:1(26-32)Online publication date: 2024
https://doi.org/10.37648/ijrst.v14i01.004
Kowald DMayr GSchedl MLex E(2023)A Study on Accuracy, Miscalibration, and Popularity Bias in RecommendationsAdvances in Bias and Fairness in Information Retrieval10.1007/978-3-031-37249-0_1(1-16)Online publication date: 15-Jul-2023
https://doi.org/10.1007/978-3-031-37249-0_1
Lacic EDuricic TFadljevic LTheiler DKowald D(2023)Uptrendz: API-Centric Real-Time Recommendations in Multi-domain SettingsAdvances in Information Retrieval10.1007/978-3-031-28241-6_23(255-261)Online publication date: 16-Mar-2023
https://doi.org/10.1007/978-3-031-28241-6_23
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Index Terms

Towards a scalable social recommender engine for online marketplaces: the case of apache solr
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Other conferences

WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web

April 2014

1396 pages

ISBN:9781450327459

DOI:10.1145/2567948

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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New York, NY, United States

Publication History

Published: 07 April 2014

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Seventh Framework Programme

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WWW '14

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IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

S Hungund T(2024)Enhanced Spark Cluster Recommendation Engine Powered by Generative AIInternational Journal of Research in Science and Technology10.37648/ijrst.v14i01.00414:1(26-32)Online publication date: 2024
https://doi.org/10.37648/ijrst.v14i01.004
Kowald DMayr GSchedl MLex E(2023)A Study on Accuracy, Miscalibration, and Popularity Bias in RecommendationsAdvances in Bias and Fairness in Information Retrieval10.1007/978-3-031-37249-0_1(1-16)Online publication date: 15-Jul-2023
https://doi.org/10.1007/978-3-031-37249-0_1
Lacic EDuricic TFadljevic LTheiler DKowald D(2023)Uptrendz: API-Centric Real-Time Recommendations in Multi-domain SettingsAdvances in Information Retrieval10.1007/978-3-031-28241-6_23(255-261)Online publication date: 16-Mar-2023
https://doi.org/10.1007/978-3-031-28241-6_23
Almeida JMonteiro ESilva LPazos Sierra AOliveira J(2020)A Recommender System to Help Discovering Cohorts in Rare Diseases2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS49503.2020.00012(25-28)Online publication date: Jul-2020
https://doi.org/10.1109/CBMS49503.2020.00012
Lacic EReiter-Haas MDuricic TSlawicek VLex EBogers TSaid ABrusilovsky PTikk D(2019)Should we embed? A study on the online performance of utilizing embeddings for real-time job recommendationsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346989(496-500)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3346989
Mehmood HGilman ECortes MKostakos PByrne AValta KTekes SRiekki J(2019)Implementing Big Data Lake for Heterogeneous Data Sources2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2019.00-37(37-44)Online publication date: Apr-2019
https://doi.org/10.1109/ICDEW.2019.00-37
Magdy AAbdelhafeez LKang YOng EMokbel M(2019)Microblogs data management: a surveyThe VLDB Journal10.1007/s00778-019-00569-6Online publication date: 18-Sep-2019
https://doi.org/10.1007/s00778-019-00569-6
Contreras DSalamo MRodriguez IPuig A(2018)Shopping Decisions Made in a Virtual World: Defining a State-Based Model of Collaborative and Conversational User-Recommender InteractionsIEEE Consumer Electronics Magazine10.1109/MCE.2017.27288197:4(26-35)Online publication date: Jul-2018
https://doi.org/10.1109/MCE.2017.2728819
Cevahir A(2017)Index partitioning through a bipartite graph model for faster similarity search in recommendation systemsInformation Systems Frontiers10.1007/s10796-016-9646-x19:5(1161-1176)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s10796-016-9646-x
Traub MKowald DLacic ESchoen PSupp GLex ELindstaedt SLey TSack H(2015)Smart booking without lookingProceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business10.1145/2809563.2809616(1-4)Online publication date: 21-Oct-2015
https://dl.acm.org/doi/10.1145/2809563.2809616
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