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A generic semantic-based framework for cross-domain recommendation

Authors:

Ignacio Fernández-Tobías,

Iván Cantador,

Marius Kaminskas,

Francesco RicciAuthors Info & Claims

HetRec '11: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems

Pages 25 - 32

https://doi.org/10.1145/2039320.2039324

Published: 27 October 2011 Publication History

Abstract

In this paper, we present an ongoing research work on the design and development of a generic knowledge-based description framework built upon semantic networks. It aims at integrating and exploiting knowledge on several domains to provide cross-domain item recommendations. More specifically, we propose an approach that automatically extracts information about two different domains, such as architecture and music, which are available in Linked Data repositories. This enables to link concepts in the two domains by means of a weighted directed acyclic graph, and to perform weight spreading on such graph to identify items in the target domain (music artists) that are related to items of the source domain (places of interest).

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Cited By

Wang YXie QTang MLi LYuan JLiu Y(2024)A Dual Perspective Framework of Knowledge-correlation for Cross-domain RecommendationACM Transactions on Knowledge Discovery from Data10.1145/365252018:6(1-28)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3652520
Chen XZhang YTsang IPan YSu J(2023)Toward Equivalent Transformation of User Preferences in Cross Domain RecommendationACM Transactions on Information Systems10.1145/352276241:1(1-31)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3522762
Richa Bedi P(2021)Trust and Distrust based Cross-domain Recommender SystemApplied Artificial Intelligence10.1080/08839514.2021.188129735:4(326-351)Online publication date: 12-Feb-2021
https://doi.org/10.1080/08839514.2021.1881297
Show More Cited By

Index Terms

A generic semantic-based framework for cross-domain recommendation
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

cover image ACM Conferences

HetRec '11: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems

October 2011

77 pages

ISBN:9781450310277

DOI:10.1145/2039320

Conference Chairs:
Iván Cantador
Universidad Autónoma de Madrid, Spain
,
Peter Brusilovsky
University of Pittsburgh
,
Tsvi Kuflik
University of Haifa, Israel

Copyright © 2011 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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2011

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Ministerio de Ciencia e Innovación
Community of Madrid

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RecSys '11

Sponsor:

SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 27, 2011

Illinois, Chicago

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Cited By

Wang YXie QTang MLi LYuan JLiu Y(2024)A Dual Perspective Framework of Knowledge-correlation for Cross-domain RecommendationACM Transactions on Knowledge Discovery from Data10.1145/365252018:6(1-28)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3652520
Chen XZhang YTsang IPan YSu J(2023)Toward Equivalent Transformation of User Preferences in Cross Domain RecommendationACM Transactions on Information Systems10.1145/352276241:1(1-31)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3522762
Richa Bedi P(2021)Trust and Distrust based Cross-domain Recommender SystemApplied Artificial Intelligence10.1080/08839514.2021.188129735:4(326-351)Online publication date: 12-Feb-2021
https://doi.org/10.1080/08839514.2021.1881297
Vikas Tyagi BKumar VSharma P(2021)Cross-Domain Recommendation Approach Based on Topic Modeling and OntologySoft Computing: Theories and Applications10.1007/978-981-16-1740-9_32(397-406)Online publication date: 31-Jul-2021
https://doi.org/10.1007/978-981-16-1740-9_32
Roitero KCarterette BMehrotra RLalmas M(2020)Leveraging Behavioral Heterogeneity Across Markets for Cross-Market Training of Recommender SystemsCompanion Proceedings of the Web Conference 202010.1145/3366424.3384362(694-702)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3384362
Alfarhood SGauch SLabille K(2019)Semantic Distance Spreading Across Entities in Linked Open DataInformation10.3390/info1001001510:1(15)Online publication date: 2-Jan-2019
https://doi.org/10.3390/info10010015
Wenige LRuhland JKejriwal MLopez VSequeda J(2019)Similarity-based knowledge graph queries for recommendation retrievalSemantic Web10.3233/SW-19035310:6(1007-1037)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.3233/SW-190353
Corno FDe Russis LMonge Roffarello A(2019)RecRulesACM Transactions on Intelligent Systems and Technology10.1145/334421110:5(1-27)Online publication date: 5-Sep-2019
https://dl.acm.org/doi/10.1145/3344211
Wang YFeng CGuo CChu YHwang JCulpepper JMoffat ABennett PLerman K(2019)Solving the Sparsity Problem in Recommendations via Cross-Domain Item Embedding Based on Co-ClusteringProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3290973(717-725)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3290973
Maneeroj SJirachanchaisiri PSuksomjit CZatloukal A(2019)Cross-Category Product Recommender System based on Multi-Criteria Rating using Diversity and Novelty Evaluation2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE.2019.8864193(193-198)Online publication date: Jul-2019
https://doi.org/10.1109/JCSSE.2019.8864193
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