research-article

Hybrid event recommendation using linked data and user diversity

Authors:

Raphaël TroncyAuthors Info & Claims

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

Pages 185 - 192

https://doi.org/10.1145/2507157.2507171

Published: 12 October 2013 Publication History

Abstract

An ever increasing number of social services offer thousands of diverse events per day. Users tend to be overwhelmed by the massive amount of information available, especially with limited browsing options perceived in many event web services. To alleviate this information overload, a recommender system becomes a vital component for assisting users selecting relevant events. However, such system faces a number of challenges owed to the the inherent complex nature of an event. In this paper, we propose a novel hybrid approach built on top of Semantic Web. On the one hand, we use a content-based system enriched with Linked Data to overcome the data sparsity, a problem induced by the transiency of events. On the other hand, we incorporate a collaborative filtering to involve the social aspect, an influential feature in decision making. This hybrid system is enhanced by the integration of a user diversity model designed to detect user propensity towards specific topics. We show how the hybridization of CB+CF systems and the integration of interest diversity features are important to improve predictions. Experimental results demonstrate the effectiveness of our approach using precision and recall measures.

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Cited By

Abdollahi SDemidova EGottschalk S(2024)Event Recommendation Through Language-Specific User Behaviour in ClickstreamsEvent Analytics across Languages and Communities10.1007/978-3-031-64451-1_8(149-168)Online publication date: 17-Jun-2024
https://doi.org/10.1007/978-3-031-64451-1_8
Abdollahi SGottschalk SDemidova E(2023)LaSERWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2022.10075975:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.websem.2022.100759
Zanon ASouza LPressato DManzato M(2022)A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendationsExpert Systems10.1111/exsy.1299139:8Online publication date: 14-Mar-2022
https://doi.org/10.1111/exsy.12991
Show More Cited By

Index Terms

Hybrid event recommendation using linked data and user diversity
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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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.

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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

Abdollahi SDemidova EGottschalk S(2024)Event Recommendation Through Language-Specific User Behaviour in ClickstreamsEvent Analytics across Languages and Communities10.1007/978-3-031-64451-1_8(149-168)Online publication date: 17-Jun-2024
https://doi.org/10.1007/978-3-031-64451-1_8
Abdollahi SGottschalk SDemidova E(2023)LaSERWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2022.10075975:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.websem.2022.100759
Zanon ASouza LPressato DManzato M(2022)A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendationsExpert Systems10.1111/exsy.1299139:8Online publication date: 14-Mar-2022
https://doi.org/10.1111/exsy.12991
Huang YWang WZhang LXu RZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Sliding Spectrum Decomposition for Diversified RecommendationProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467108(3041-3049)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467108
Mikhridinova NBadasian AAldaghamin AWolff C(2021)Transforming Conferences to an Online Format: Framework and Practices2021 IEEE International Conference on Smart Information Systems and Technologies (SIST)10.1109/SIST50301.2021.9465947(1-8)Online publication date: 28-Apr-2021
https://doi.org/10.1109/SIST50301.2021.9465947
Dadoun ADefoin-Platel MFiig TLandra CTroncy R(2021)How recommender systems can transform airline offer construction and retailingJournal of Revenue and Pricing Management10.1057/s41272-021-00313-220:3(301-315)Online publication date: 20-Mar-2021
https://doi.org/10.1057/s41272-021-00313-2
Liu HShi XLi GLiu SPan L(2021)Ontology Guided Sparse Tensor Factorization for joint recommendation with hierarchical relationshipsPersonal and Ubiquitous Computing10.1007/s00779-020-01489-x26:4(983-993)Online publication date: 7-Jan-2021
https://dl.acm.org/doi/10.1007/s00779-020-01489-x
Ojagh SMalek MSaeedi S(2020)A Social–Aware Recommender System Based on User’s Personal Smart DevicesISPRS International Journal of Geo-Information10.3390/ijgi90905199:9(519)Online publication date: 30-Aug-2020
https://doi.org/10.3390/ijgi9090519
De Santo AHolzer ABernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Interacting with Linked Data: A Survey from the SIGCHI PerspectiveExtended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3334480.3382909(1-12)Online publication date: 25-Apr-2020
https://dl.acm.org/doi/10.1145/3334480.3382909
Huang WLi QMeng S(2020)KG2Rec: LSH-CF recommendation method based on knowledge graph for cloud servicesWireless Networks10.1007/s11276-020-02387-zOnline publication date: 5-Jun-2020
https://doi.org/10.1007/s11276-020-02387-z
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