short-paper

Using Linked Open Data in Recommender Systems

Authors:

Ladislav Peska,

Peter VojtasAuthors Info & Claims

WIMS '15: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics

Article No.: 17, Pages 1 - 6

https://doi.org/10.1145/2797115.2797128

Published: 13 July 2015 Publication History

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Abstract

In this paper, we present our work in progress on using LOD data to enhance recommending on existing e-commerce sites. We imagine a situation of e-commerce website employing content-based or hybrid recommendation. Such recommending algorithms need relevant object attributes to produce useful recommendations. However, on some domains, usable attributes may be difficult to fill in manually and yet accessible from LOD cloud.

A pilot study was conducted on the domain of secondhand bookshops. In this domain, recommending is extraordinary difficult because of high ratio between objects and users, lack of significant attributes and limited availability of items. Both collaborative filtering and content-based recommendation applicability is questionable under this conditions. We queried both Czech and English language edition of DBPedia in order to receive additional information about objects (books) and used various recommending algorithms to learn user preferences. Our approach is general and can be applied on other domains as well.

Proposed methods were tested in an off-line recommending scenario with promising results; however there are a lot of challenges for the future work including more complex algorithm analysis, improving SPARQL queries or improving DBPedia matching rules and resource identification.

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Cited By

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Valencia-Martinez CGaona-García PMontenegro-Marin C(2023)Design of a trust system for e-commerce platforms based on quality dimensions for linked open datasetsJournal of Information Systems Engineering and Management10.55267/iadt.07.127418:1(18756)Online publication date: 2023
https://doi.org/10.55267/iadt.07.12741
Zitouni H(2022)On Solving Cold Start Problem in Recommender Systems Using Web of Data2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)10.1109/PAIS56586.2022.9946899(1-8)Online publication date: 12-Oct-2022
https://doi.org/10.1109/PAIS56586.2022.9946899
Rehage G(2019)Ontologie-basiertes WBS für die ArbeitsplanungIntelligente Arbeitsvorbereitung auf Basis virtueller Werkzeugmaschinen10.1007/978-3-662-58020-2_3(41-90)Online publication date: 6-Apr-2019
https://doi.org/10.1007/978-3-662-58020-2_3
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Index Terms

Using Linked Open Data in Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

WIMS '15: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics

July 2015

176 pages

ISBN:9781450332934

DOI:10.1145/2797115

Conference Chair:
Rajendra Akerkar
Western Norway Research Institute, Norway
,
General Chair:
Marios Dikaiakos
University of Cyprus, Cyprus
,
Program Chairs:
Achilleas Achilleos
University of Cyprus, Cyprus
,
Tope Omitola
University of Southampton, United Kingdom

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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WNRI: Western Norway Research Institute
University of Cyprus

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2015

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WIMS '15

WIMS '15: 5th International Conference on Web Intelligence, Mining and Semantics

July 13 - 15, 2015

Larnaca, Cyprus

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Overall Acceptance Rate 140 of 278 submissions, 50%

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Valencia-Martinez CGaona-García PMontenegro-Marin C(2023)Design of a trust system for e-commerce platforms based on quality dimensions for linked open datasetsJournal of Information Systems Engineering and Management10.55267/iadt.07.127418:1(18756)Online publication date: 2023
https://doi.org/10.55267/iadt.07.12741
Zitouni H(2022)On Solving Cold Start Problem in Recommender Systems Using Web of Data2022 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS)10.1109/PAIS56586.2022.9946899(1-8)Online publication date: 12-Oct-2022
https://doi.org/10.1109/PAIS56586.2022.9946899
Rehage G(2019)Ontologie-basiertes WBS für die ArbeitsplanungIntelligente Arbeitsvorbereitung auf Basis virtueller Werkzeugmaschinen10.1007/978-3-662-58020-2_3(41-90)Online publication date: 6-Apr-2019
https://doi.org/10.1007/978-3-662-58020-2_3
Fayyaz NUllah IKhusro S(2018)On the Current State of Linked Open DataInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.201810010614:4(110-128)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.4018/IJSWIS.2018100106
ZITOUNI HMESHOUL SKADI A(2018)Linked Open Data for New Item Problem Solving in Collaborative Recommender Systems2018 International Conference on Smart Communications in Network Technologies (SaCoNeT)10.1109/SaCoNeT.2018.8585443(217-221)Online publication date: Oct-2018
https://doi.org/10.1109/SaCoNeT.2018.8585443
Pereira Fressato EFortes da Costa AGarcia Manzato M(2018)Similarity-Based Matrix Factorization for Item Cold-Start in Recommender Systems2018 7th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2018.00066(342-347)Online publication date: Oct-2018
https://doi.org/10.1109/BRACIS.2018.00066
Alfarhood SGauch SLabille K(2017)Employing Link Differentiation in Linked Data Semantic DistanceKnowledge Engineering and Semantic Web10.1007/978-3-319-69548-8_13(175-191)Online publication date: 18-Oct-2017
https://doi.org/10.1007/978-3-319-69548-8_13
Rehage GJoppen RGausemeier J(2016)Perspective on the Design of a Knowledge-based System Embedding Linked Data for Process PlanningProcedia Technology10.1016/j.protcy.2016.08.03626(267-276)Online publication date: 2016
https://doi.org/10.1016/j.protcy.2016.08.036

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References

Cited By

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Recommendations

Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback

Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on Linked Open Data

Context-Based Rating Prediction using Collaborative Filtering and Linked Open Data

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