HELPeR: An Interactive Recommender System for Ovarian Cancer Patients and Caregivers

Rahdari, Behnam; Brusilovsky, Peter; He, Daqing; Thaker, Khushboo; Luo, Zhimeng; Lee, Young Ji

doi:10.1145/3523227.3551471

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Rahdari et al (2021) also explored how to improve interactive control in MLRI by allowing users to fine-tune the importance of the topics represented by individual carousels. In a recent publication, they further demonstrated the successful use of carousels in a more practical domain, i.e., medical advice (Rahdari et al, 2022a). Without user studies, however, the empirical basis for the design of MLRI still remains weak, especially in light of other domains, where the user experience may be different depending on, e.g., item complexity and user familiarity.…”

Section: Algorithms Metrics and Models For Carousel Recommendationsmentioning

confidence: 99%

Multi-list interfaces for recommender systems: survey and future directions

Loepp

2023

Front. Big Data

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For a long time, recommender systems presented their results in the form of simple item lists. In recent years, however, multi-list interfaces have become the de-facto standard in industry, presenting users with numerous collections of recommendations, one below the other, each containing items with common characteristics. Netflix's interface, for instance, shows movies from certain genres, new releases, and lists of curated content. Spotify recommends new songs and albums, podcasts on specific topics, and what similar users are listening to. Despite their popularity, research on these so-called “carousels” is still limited. Few authors have investigated how to simulate the user behavior and how to optimize the recommendation process accordingly. The number of studies involving users is even smaller, with sometimes conflicting results. Consequently, little is known about how to design carousel-based interfaces for achieving the best user experience. This mini review aims to organize the existing knowledge and outlines directions that may improve the multi-list presentation of recommendations in the future.

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Section: Algorithms Metrics and Models For Carousel Recommendationsmentioning

confidence: 99%

Multi-list interfaces for recommender systems: survey and future directions

Loepp

2023

Front. Big Data

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“…Conversational recommender systems [26,38,41] and explanation [24,53,56] have been primarily discussed with a strong focus on user behaviors and perception [63]. The last edition in 2022 brought the discussion back to the use of cognitive aspects in recommender systems [23,39,58,59] and user interfaces, pushing the discussion toward new frontiers of dynamic and interactive interfaces [43,55,66]. In Fig.…”

Section: History and Research Trends Of The Workhopmentioning

confidence: 99%

10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’23)

Brusilovsky,

De Gemmis,

Felfernig

et al. 2023

Proceedings of the 17th ACM Conference on Recommender Systems

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“…As the Web has matured and developed, so have recommender systems, and specifically recommender system applications in health (Alhijawi et al 2022). Recent applications include drug recommendation (Zheng et al 2022), diabetes treatment recommendation (Oh et al 2022), cancer drug response prediction (Wang et al 2022), and recommendations for cancer patients and caregivers (Rahdari et al 2022), to name a few.…”

Section: History and Trendsmentioning

confidence: 99%

Research directions in recommender systems for health and well-being

Schäfer

Said

Trattner

2022

User Model User-Adap Inter

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Recommender systems have been put to use in the entertainment and e-commerce domains for decades, and in these decades, recommender systems have grown and matured into reliable and ubiquitous systems in today's digital landscape. Relying on this maturity, the application of recommender systems for health and well-being has seen a rise in recent years, paving the way for tailored and personalized systems that support caretakers, caregivers, and other users in the health domain. In this introduction, we give a brief overview of the stakes, the requirements, and the possibilities that recommender systems for health and well-being bring. KeywordsHealth recommender system • Health-aware computing • Health-aware information systems • Recommender systems • Well-being B Hanna Hauptmann

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HELPeR: An Interactive Recommender System for Ovarian Cancer Patients and Caregivers

Cited by 8 publications

References 9 publications

Multi-list interfaces for recommender systems: survey and future directions

Multi-list interfaces for recommender systems: survey and future directions

10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’23)

Research directions in recommender systems for health and well-being

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