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StoryTime: eliciting preferences from children for book recommendations

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Maria Soledad PeraAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 544 - 545

https://doi.org/10.1145/3298689.3347048

Published: 10 September 2019 Publication History

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Abstract

We present StoryTime, a book recommender for children. Our web-based recommender is co-designed with children and uses images to elicit their preferences. By building on existing solutions related to both visual interfaces and book recommendation strategies for children, StoryTime can generate suggestions without historical data or adult guidance. We discuss the benefits of StoryTime as a starting point for further research exploring the cold start problem, incorporating historical data, and needs related to children as a complex audience to enhance the recommendation process.

References

[1]

Ali Darejeh and Dalbir Singh. 2013. A review on user interface design principles to increase software usability for users with less computer literacy. Vol. 9. 1443--1450 pages.

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[2]

Tatiana Gossen. 2016. Search engines for children: search user interfaces and information-seeking behaviour. Springer.

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[3]

Maria Soledad Pera and Yiu-Kai Ng. 2014. Automating Readers' Advisory to Make Book Recommendations for K-12 Readers. In Proc. of ACM RecSys. ACM, 9--16.

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[4]

Maria Soledad Pera and Yiu Kai Ng. 2014. How Can We Help Our K-12 Teachers?: Using a Recommender to Make Personalized Book Suggestions. In Proc. of IEEE/WIC/ACM WI IAT. IEEE, 335--342.

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Beyhan YPera M(2023)Covering Covers: Characterization Of Visual Elements Regarding SleevesAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597404(28-33)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597404
Mataoui MSebbak FSidhoum AHarbi TSenouci MBelmessous K(2023)A hybrid recommendation system for researchgate academic social networkSocial Network Analysis and Mining10.1007/s13278-023-01056-113:1Online publication date: 19-Mar-2023
https://doi.org/10.1007/s13278-023-01056-1
Ng Y(2023)Read to grow: exploring metadata of books to make intriguing book recommendations for teenage readersKnowledge and Information Systems10.1007/s10115-023-01907-565:11(4537-4562)Online publication date: 2-Jun-2023
https://doi.org/10.1007/s10115-023-01907-5
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StoryTime: eliciting preferences from children for book recommendations

Recommendations

A survey of active learning in collaborative filtering recommender systems

In collaborative filtering recommender systems user's preferences are expressed as ratings for items, and each additional rating extends the knowledge of the system and affects the system's recommendation accuracy. In general, the more ratings are ...
Less is More: Sparse Representative based Preference Elicitation for Cold Start Recommendation
ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

Cold start recommendation is a challenging but crucial problem for recommender systems. Preference Elicitation, as a commonly used approach to address the problem, solicits preference of cold user by interviewing them with some elaborately selected ...
User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items
Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. ...

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Information & Contributors

Information

Published In

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

September 2019

635 pages

ISBN:9781450362436

DOI:10.1145/3298689

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 10 September 2019

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National Science Foundation

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RecSys '19

RecSys '19: Thirteenth ACM Conference on Recommender Systems

September 16 - 20, 2019

Copenhagen, Denmark

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RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;

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

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Beyhan YPera M(2023)Covering Covers: Characterization Of Visual Elements Regarding SleevesAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597404(28-33)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597404
Mataoui MSebbak FSidhoum AHarbi TSenouci MBelmessous K(2023)A hybrid recommendation system for researchgate academic social networkSocial Network Analysis and Mining10.1007/s13278-023-01056-113:1Online publication date: 19-Mar-2023
https://doi.org/10.1007/s13278-023-01056-1
Ng Y(2023)Read to grow: exploring metadata of books to make intriguing book recommendations for teenage readersKnowledge and Information Systems10.1007/s10115-023-01907-565:11(4537-4562)Online publication date: 2-Jun-2023
https://doi.org/10.1007/s10115-023-01907-5
Gao SNg Y(2021)Analyzing the Preferences and Personal Needs of Teenage Readers to Make Book RecommendationsIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3486622.3493972(463-468)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3486622.3493972
Stitini OKaloun SBencharef O(2021)The Recommendation of a Practical Guide for Doctoral Students Using Recommendation System Algorithms in the Education FieldInnovations in Smart Cities Applications Volume 410.1007/978-3-030-66840-2_19(240-254)Online publication date: 13-Feb-2021
https://doi.org/10.1007/978-3-030-66840-2_19

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Abstract

References

Cited By

Index Terms

Recommendations

A survey of active learning in collaborative filtering recommender systems

Less is More: Sparse Representative based Preference Elicitation for Cold Start Recommendation

User-Specific Feature-Based Similarity Models for Top-n Recommendation of New Items

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