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An open framework for multi-source, cross-domain personalisation with semantic interest graphs

Author:

Benjamin HeitmannAuthors Info & Claims

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

Pages 313 - 316

https://doi.org/10.1145/2365952.2366030

Published: 09 September 2012 Publication History

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Abstract

Cross-domain recommendations are currently available in closed, proprietary social networking ecosystems such as Facebook, Twitter and Google+. I propose an open framework as an alternative, which enables cross-domain recommendations with domain-agnostic user profiles modeled as semantic interest graphs. This novel framework covers all parts of a recommender system. It includes an architecture for privacy-enabled profile exchange, a distributed and domain-agnostic user model and a cross-domain recommendation algorithm. This enables users to receive recommendations for a target domain (e.g. food) based on any kind of previous interests.

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Ding XWu ZLi M(2022)Clustering Merchants and Accurate Marketing of Products Using the Segmentation Tree Vector Space ModelMathematical Problems in Engineering10.1155/2022/73531512022(1-11)Online publication date: 29-Mar-2022
https://doi.org/10.1155/2022/7353151
Du HTang YCheng Z(2022)An efficient joint framework for interacting knowledge graph and item recommendationKnowledge and Information Systems10.1007/s10115-022-01808-z65:4(1685-1712)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1007/s10115-022-01808-z
Cao YWang XHe XHu ZChua T(2019)Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User PreferencesThe World Wide Web Conference10.1145/3308558.3313705(151-161)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313705
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Index Terms

An open framework for multi-source, cross-domain personalisation with semantic interest graphs
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information

Published In

RecSys '12: Proceedings of the sixth ACM conference on Recommender systems

September 2012

376 pages

ISBN:9781450312707

DOI:10.1145/2365952

General Chairs:
Pádraig Cunningham
University College Dublin, Ireland
,
Neil Hurley
University College Dublin, Ireland
,
Program Chairs:
Ido Guy
IBM Haifa Research Laboratory, Israel
,
Sarabjot Singh Anand
University of Warwick, England

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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Association for Computing Machinery

New York, NY, United States

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Published: 09 September 2012

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Short-paper

Conference

RecSys '12

Sponsor:

SIGCHI

RecSys '12: Sixth ACM Conference on Recommender Systems

September 9 - 13, 2012

Dublin, Ireland

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RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all

Ding XWu ZLi M(2022)Clustering Merchants and Accurate Marketing of Products Using the Segmentation Tree Vector Space ModelMathematical Problems in Engineering10.1155/2022/73531512022(1-11)Online publication date: 29-Mar-2022
https://doi.org/10.1155/2022/7353151
Du HTang YCheng Z(2022)An efficient joint framework for interacting knowledge graph and item recommendationKnowledge and Information Systems10.1007/s10115-022-01808-z65:4(1685-1712)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1007/s10115-022-01808-z
Cao YWang XHe XHu ZChua T(2019)Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User PreferencesThe World Wide Web Conference10.1145/3308558.3313705(151-161)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313705
Ai QAzizi VChen XZhang Y(2018)Learning Heterogeneous Knowledge Base Embeddings for Explainable RecommendationAlgorithms10.3390/a1109013711:9(137)Online publication date: 13-Sep-2018
https://doi.org/10.3390/a11090137
Durao FBridge D(2018)A Linked Data Browser with Recommendations2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2018.00038(189-196)Online publication date: Nov-2018
https://doi.org/10.1109/ICTAI.2018.00038
Corbellini AGodoy DMateos CSchiaffino SZunino A(2018)DPMFuture Generation Computer Systems10.1016/j.future.2017.02.02578:P1(474-480)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.future.2017.02.025
Piao GBreslin J(2018)Transfer Learning for Item Recommendations and Knowledge Graph Completion in Item Related Domains via a Co-Factorization ModelThe Semantic Web10.1007/978-3-319-93417-4_32(496-511)Online publication date: 3-Jun-2018
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Corbellini AGodoy DMateos CSchiaffino SZunino A(2017)Task Scheduling for Processing Big Graphs in Heterogeneous Commodity ClustersHigh Performance Computing10.1007/978-3-319-73353-1_16(235-249)Online publication date: 28-Dec-2017
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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
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Piao GBreslin J(2017)Factorization Machines Leveraging Lightweight Linked Open Data-Enabled Features for Top-N RecommendationsWeb Information Systems Engineering – WISE 201710.1007/978-3-319-68786-5_33(420-434)Online publication date: 4-Oct-2017
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