research-article

SIRUP: Serendipity In Recommendations via User Perceptions

Authors:

Valentina Maccatrozzo,

Manon Terstall,

Guus SchreiberAuthors Info & Claims

IUI '17: Proceedings of the 22nd International Conference on Intelligent User Interfaces

Pages 35 - 44

https://doi.org/10.1145/3025171.3025185

Published: 07 March 2017 Publication History

Abstract

In this paper, we propose a model to operationalise serendipity in content-based recommender systems. The model, called SIRUP, is inspired by the Silvia's curiosity theory, based on the fundamental theory of Berlyne, aims at (1) measuring the novelty of an item with respect to the user profile, and (2) assessing whether the user is able to manage such level of novelty (coping potential). The novelty of items is calculated with cosine similarities between items, using Linked Open Data paths. The coping potential of users is estimated by measuring the diversity of the items in the user profile. We deployed and evaluated the SIRUP model in a use case with TV recommender using BBC programs dataset. Results show that the SIRUP model allows us to identify serendipitous recommendations, and, at the same time, to have 71% precision.

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Cited By

Nalis ISippl TKolb TNeidhardt JBoratto LGena CMarras MGermanakos PPopescus E(2024)Navigating Serendipity - An Experimental User Study On The Interplay of Trust and Serendipity In Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664901(386-393)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664901
Xu ZMatsumura A(2024)A Serendipitous Recommendation System Considering User CuriosityInformation Integration and Web Intelligence10.1007/978-3-031-78093-6_3(33-48)Online publication date: 4-Dec-2024
https://doi.org/10.1007/978-3-031-78093-6_3
XU ZZHOU JTAKADA HMATSUMURA ASATOH T(2023)Serendipitous User Recommendation in Twitter by Consider Unexpected and Useful InterestsTwitterにおける興味の意外性と有用性を考慮したセレンディピティなユーザの推薦Joho Chishiki Gakkaishi10.2964/jsik_2023_02733:3(267-288)Online publication date: 30-Sep-2023
https://doi.org/10.2964/jsik_2023_027
Show More Cited By

Index Terms

SIRUP: Serendipity In Recommendations via User Perceptions
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

IUI '17: Proceedings of the 22nd International Conference on Intelligent User Interfaces

March 2017

654 pages

ISBN:9781450343480

DOI:10.1145/3025171

General Chairs:
George A. Papadopoulos
The University of Cyprus, Cyprus
,
Tsvi Kuflik
The University of Haifa, Israel
,
Program Chairs:
Fang Chen
Data61, CSIRO Australia
,
Carlos Duarte
The University of Lisbon, Portugal
,
Wai-Tat Fu
The University of Illinois at Urbana-Champaign, USA

Copyright © 2017 ACM.

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Published: 07 March 2017

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Cited By

Nalis ISippl TKolb TNeidhardt JBoratto LGena CMarras MGermanakos PPopescus E(2024)Navigating Serendipity - An Experimental User Study On The Interplay of Trust and Serendipity In Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664901(386-393)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664901
Xu ZMatsumura A(2024)A Serendipitous Recommendation System Considering User CuriosityInformation Integration and Web Intelligence10.1007/978-3-031-78093-6_3(33-48)Online publication date: 4-Dec-2024
https://doi.org/10.1007/978-3-031-78093-6_3
XU ZZHOU JTAKADA HMATSUMURA ASATOH T(2023)Serendipitous User Recommendation in Twitter by Consider Unexpected and Useful InterestsTwitterにおける興味の意外性と有用性を考慮したセレンディピティなユーザの推薦Joho Chishiki Gakkaishi10.2964/jsik_2023_02733:3(267-288)Online publication date: 30-Sep-2023
https://doi.org/10.2964/jsik_2023_027
Maccatrozzo VKuhn TCeolin DVan Ossenbruggen J(2023)The Role of Serendipity in User-Curated Music PlaylistsProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627552(140-147)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627552
Yan DChen L(2023)The Influence of Personality Traits on User Interaction with Recommendation InterfacesACM Transactions on Interactive Intelligent Systems10.1145/355877213:1(1-39)Online publication date: 10-Mar-2023
https://dl.acm.org/doi/10.1145/3558772
Tokutake YOkamoto K(2023)Serendipity-Oriented Recommender System with Dynamic Unexpectedness Prediction2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394368(1247-1252)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394368
Park HOh KOh U(2022)PSR: Probabilistic Serendipitous Recommendations2022 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI58124.2022.00144(790-795)Online publication date: Dec-2022
https://doi.org/10.1109/CSCI58124.2022.00144
Pramod DBafna P(2022)Conversational recommender systems techniques, tools, acceptance, and adoptionExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117539203:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117539
Wang NChen L(2022)How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysisUser Modeling and User-Adapted Interaction10.1007/s11257-022-09350-x33:3(727-765)Online publication date: 1-Dec-2022
https://doi.org/10.1007/s11257-022-09350-x
Nixon LFoss JApostolidis KMezaris V(2022)Data-driven personalisation of television content: a surveyMultimedia Systems10.1007/s00530-022-00926-628:6(2193-2225)Online publication date: 23-Apr-2022
https://doi.org/10.1007/s00530-022-00926-6
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