Recommender systems in the healthcare domain: state-of-the-art and research issues

Tran, Thi Ngoc Trang; Felfernig, Alexander; Trattner, Christoph; Holzinger, Andreas

doi:10.1007/s10844-020-00633-6

Cited by 172 publications

(59 citation statements)

References 76 publications

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“…Tran et al [30] provided a systematic overview of existing research on recommender systems in the healthcare domain, paying attention to different recommendation scenarios and approaches. Among the issues raised in the paper, we assume that the problem of constructing user profile is interesting and worth being investigated in the scope of the HybridRec work.…”

Section: Boosting Techniques Recommender Systemsmentioning

confidence: 99%

HybridRec: A recommender system for tagging GitHub repositories

et al. 2022

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Software repositories are increasingly essential to support the management of typical artifacts building up projects, including source code, documentation, and bug reports. GitHub is at the forefront of this kind of platforms, providing developer with a reservoir of code contained in more than 28M repositories. To help developers find the right artifacts, GitHub uses topics, which are short texts assigned to the stored artifacts. However, assigning inappropriate topics to a repository might hamper its popularity and reachability. In our previous work, we implemented MNBN and TopFilter to recommend GitHub topics. MNBN exploits a stochastic network to predict topics, while TopFilter relies on a syntactic-based function to recommend topics. In this paper, we extend our work by building HybridRec, a recommender system based on stochastic and collaborative-filtering techniques to generate more relevant topics. To deal with unbalanced datasets, we employ a Complement Naïve Bayesian Network (CNBN). Furthermore, we apply a preprocessing phase to clean and refine the input data before feeding the recommendation engine. An empirical evaluation demonstrates that HybridRec outperforms three state-of-the-art baselines, obtaining a better performance with respect to various metrics. We conclude that the conceived framework can be used to help developers increase their projects’ visibility.

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Section: Boosting Techniques Recommender Systemsmentioning

confidence: 99%

HybridRec: A recommender system for tagging GitHub repositories

et al. 2022

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“…Typical methods for recommendation include contentbased, collaborative, demographic, and knowledge-based filtering and hybrid approaches [23], [31], [32], all of which have been used in HRSs supporting behavior change [5], [17], [18]. In content-based filtering, items that are similar to those rated positively by the user are recommended.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation

et al. 2022

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Health recommender systems (HRSs) have the potential to effectively personalize well-being related behavior change interventions to the needs of individuals. However, personalization is often considered from a narrow perspective, and the underlying user features are inconsistent across HRSs. Particularly, theory-based determinants of behavior and the variety of lifestyle domains influencing wellbeing are poorly addressed. We propose a comprehensive, theory-based framework of user features, a virtual individual (VI) model, to support the extensive personalization of digital well-being interventions. We introduce a prototype HRS (With-Me HRS), which uses knowledge-based filtering and recommends behavior change objectives and activities from several lifestyle domains. With-Me HRS realizes a minimum set of important VI model features related to well-being, lifestyle, and behavioral intention. We report the preliminary validity and usefulness of the HRS, evaluated in a real-life health-coaching program with 50 participants. The recommendations were used in decision-making for half of the participants and hidden for others. For 73% of the participants (85% with visible vs. 62% with hidden recommendations), at least one of the recommended activities was included into their coaching plans. The HRS reduced coaches' perceived effort in identifying appropriate coaching tasks for the participants (effect size: Vargha-Delaney Â=0.71, 95% CI 0.59-0.84) but not in identifying behavior change objectives. From the participants' perspective, the quality of coaching improved (effect size for one of three quality metrics: Â=0.71, 95% CI 0.57-0.83). These results provide a baseline for testing the influence of additional user model features on the validity of recommendations generated by knowledge-based multi-domain HRSs.

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“…In a variety of lifestyle applications and services, food recommendation plays an important role as a tool assisting users to change behavior and adopt healthy lifestyle [13]- [15]. Typically, food recommendation attempt to provide the user with a personalized food recommendation in terms of recipes, scale of change and time required to achieve specific objectives that might be associated with diet requirement or any lifestyle demand [16]- [18]. Traditionally, research in food recommendation has seen little attention when compared to recommendation in other leisure and entertainment fields (e.g., music, book, shopping recommendation systems), possibly due to cultural barriers and difficulty to predict what people might like to eat.…”

Section: Introductionmentioning

confidence: 99%

A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering

2022

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Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous systems, such as ignoring food ingredients, time factor, cold start users, cold start food items and community aspects. The proposed method involves two phases: food content-based recommendation and user-based recommendation. Graph clustering is used in the first phase, and a deep-learning based approach is used in the second phase to cluster both users and food items. Besides a holistic-like approach is employed to account for time and user-community related issues in a way that improves the quality of the recommendation provided to the user. We compared our model with a set of state-of-the-art recommender-systems using five distinct performance metrics: Precision, Recall, F1, AUC and NDCG. Experiments using dataset extracted from "Allrecipes.com" demonstrated that the developed food recommender-system performed best.

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Recommender systems in the healthcare domain: state-of-the-art and research issues

Cited by 172 publications

References 76 publications

HybridRec: A recommender system for tagging GitHub repositories

HybridRec: A recommender system for tagging GitHub repositories

A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation

A Novel Time-Aware Food Recommender-System Based on Deep Learning and Graph Clustering

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