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Top-N recommendations from implicit feedback leveraging linked open data

Authors:

Vito Claudio Ostuni,

Tommaso Di Noia,

Eugenio Di Sciascio,

Roberto MirizziAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 85 - 92

https://doi.org/10.1145/2507157.2507172

Published: 12 October 2013 Publication History

Abstract

The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper we present SPrank, a novel hybrid recommendation algorithm able to compute top-N item recommendations from implicit feedback exploiting the information available in the so called Web of Data. We leverage DBpedia, a well-known knowledge base in the LOD compass, to extract semantic path-based features and to eventually compute recommendations using a learning to rank algorithm. Experiments with datasets on two different domains show that the proposed approach outperforms in terms of prediction accuracy several state-of-the-art top-N recommendation algorithms for implicit feedback in situations affected by different degrees of data sparsity.

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Cited By

Bellini VDi Sciascio EDonini FPomo CRagone ASchiavone A(2024)A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencodersJournal of Intelligent Information Systems10.1007/s10844-023-00830-z62:3(787-807)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-023-00830-z
Gupta MSaini P(2023)Knowledge Graphs for Explaination of Black-Box Recommender SystemDeep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing10.2174/9789815079210123010015(183-205)Online publication date: 20-Aug-2023
https://doi.org/10.2174/9789815079210123010015
Meng CZhao ZGuo WZhang YWu HGao CLi DLi XTang R(2023)Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/360636942:1(1-27)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1145/3606369
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Index Terms

Top-N recommendations from implicit feedback leveraging linked open data
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 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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Publication History

Published: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

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RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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Cited By

Bellini VDi Sciascio EDonini FPomo CRagone ASchiavone A(2024)A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencodersJournal of Intelligent Information Systems10.1007/s10844-023-00830-z62:3(787-807)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-023-00830-z
Gupta MSaini P(2023)Knowledge Graphs for Explaination of Black-Box Recommender SystemDeep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing10.2174/9789815079210123010015(183-205)Online publication date: 20-Aug-2023
https://doi.org/10.2174/9789815079210123010015
Meng CZhao ZGuo WZhang YWu HGao CLi DLi XTang R(2023)Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior RecommendationACM Transactions on Information Systems10.1145/360636942:1(1-27)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1145/3606369
Zhang LLiu PGulla J(2023)Recommending on graphs: a comprehensive review from a data perspectiveUser Modeling and User-Adapted Interaction10.1007/s11257-023-09359-w33:4(803-888)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s11257-023-09359-w
Kang HKocielnik RHead AYang JLatzke MKittur AWeld DDowney DBragg J(2022)From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social NetworksProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517470(1-23)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517470
Yulion DYahya BLewi Engel V(2022)Building a robust transition matrix using causal matrix for route recommendationProcedia Computer Science10.1016/j.procs.2021.12.199197(768-775)Online publication date: 2022
https://doi.org/10.1016/j.procs.2021.12.199
Ge HLi QMeng SHou J(2022)KPG4Rec: Knowledge Property-Aware Graph for Recommender SystemsCloud Computing10.1007/978-3-030-99191-3_9(107-122)Online publication date: 23-Mar-2022
https://doi.org/10.1007/978-3-030-99191-3_9
Abdullah NRasheed RNasir MRahman M(2021)Eliciting Auxiliary Information for Cold Start User Recommendation: A SurveyApplied Sciences10.3390/app1120960811:20(9608)Online publication date: 15-Oct-2021
https://doi.org/10.3390/app11209608
Yang XHuan ZZhai YLin T(2021)Research of Personalized Recommendation Technology Based on Knowledge GraphsApplied Sciences10.3390/app1115710411:15(7104)Online publication date: 31-Jul-2021
https://doi.org/10.3390/app11157104
Barros MMoitinho ACouto F(2021)Hybrid semantic recommender system for chemical compounds in large-scale datasetsJournal of Cheminformatics10.1186/s13321-021-00495-213:1Online publication date: 23-Feb-2021
https://doi.org/10.1186/s13321-021-00495-2
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