Malmö University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
FlowRec: Prototyping Session-based Recommender Systemsin Streaming Mode
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-9767-5324
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-1342-8618
2020 (English)In: PAKDD 2020: Advances in Knowledge Discovery and Data Mining, Springer, 2020Conference paper, Published paper (Refereed)
Abstract [en]

Despite the increasing interest towards session-based and streaming recommender systems, there is still a lack of publicly available evaluation frameworks supporting both these paradigms. To address the gap, we propose FlowRec — an extension of the streaming framework Scikit-Multiflow, which opens plentiful possibilities for prototyping recommender systems operating on sessionized data streams, thanks to the underlying collection of incremental learners and support for real-time performance tracking. We describe the extended functionalities of the adapted prequential evaluation protocol, and develop a competitive recommendation algorithm on top of Scikit-Multiflow’s implementation of a Hoeffding Tree. We compare our algorithm to other known baselines for the next-item prediction task across three different domains.

Place, publisher, year, edition, pages
Springer, 2020.
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12084
Keywords [en]
Streaming recommendations, Session-based recommendations, Prequential evaluation, Online learning, Hoeffding Tree
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-17191DOI: 10.1007/978-3-030-47426-3_6ISI: 000716986400006Scopus ID: 2-s2.0-85085731034ISBN: 978-3-030-47425-6 (print)ISBN: 978-3-030-47426-3 (electronic)OAI: oai:DiVA.org:mau-17191DiVA, id: diva2:1429027
Conference
24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020
Available from: 2020-05-07 Created: 2020-05-07 Last updated: 2024-02-05Bibliographically approved
In thesis
1. Sociotechnical Aspects of Automated Recommendations: Algorithms, Ethics, and Evaluation
Open this publication in new window or tab >>Sociotechnical Aspects of Automated Recommendations: Algorithms, Ethics, and Evaluation
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Recommender systems are algorithmic tools that assist users in discovering relevant items from a wide range of available options. Along with the apparent user value in mitigating the choice overload, they have an important business value in boosting sales and customer retention. Last, but not least, they have brought a substantial research value to the algorithm developments of the past two decades, mainly in the academic community. This thesis aims to address some of the aspects that are important to consider when recommender systems pave their way towards real-life applications.

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2020. p. 238
Series
Studies in Computer Science ; 9
Keywords
recommender systems, recommendations, matchmaking, recommendation ethics
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-13750 (URN)10.24834/isbn.9789178770755 (DOI)978-91-7877-074-8 (ISBN)978-91-7877-075-5 (ISBN)
Public defence
2020-05-08, Auditorium C, C0E11, Niagara buildning, Nordenskiöldsgatan 1, Malmö, 13:00 (English)
Opponent
Supervisors
Available from: 2020-03-11 Created: 2020-03-08 Last updated: 2024-02-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Paraschakis, DimitrisNilsson, Bengt J.

Search in DiVA

By author/editor
Paraschakis, DimitrisNilsson, Bengt J.
By organisation
Department of Computer Science and Media Technology (DVMT)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 87 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf