Launched in 2016, the Health Recommender Systems Workshop (HealthRecSys) rapidly became a central forum for discussing the transformative capabilities of personalized recommender systems within the health and care sectors. Despite the unforeseen pause due to the COVID-19 pandemic and other challenges, the workshop’s influence persisted through its vibrant community and publications. Our aim with the 6th HealthRecSys is to reignite these conversations and provide a forward-thinking platform that revisits the foundational elements that have contributed to the field’s growth. However, the workshop aspires to do more by infusing new perspectives and tackling the most pressing global challenges and technological innovations head-on with contemporary themes such as the impact of global health crises, generative AI models, personalized and self-managed care, and the increasing focus on health equity. HealthRecSys is dedicated to strengthening the network of researchers working on health recommender systems, drawing participants from an array of health and care domains. Through our combined interactive and paper based workshop format, we aim at cultivating a cross-disciplinary community that promotes collaboration among recommender systems specialists, healthcare professionals, ethicists, and policymakers, among others.

Despite their proven various benefits, Recommender Systems can cause or amplify certain undesired effects. In this paper, we focus on Popularity Bias, i.e., the tendency of a recommender system to utilize the effect of recommending popular items to the user. Prior research has studied the negative impact of this type of bias on individuals and society as a whole and proposed various approaches to mitigate this in various domains. However, almost all works adopted offline methodologies to evaluate the effectiveness of the proposed approaches. Unfortunately, such offline simulations can potentially be rather simplified and unable to capture the full picture. To contribute to this line of research and given a particular lack of knowledge about how debiasing approaches work not only offline, but online as well, we present in this paper the results of user study on a national broadcaster movie streaming platform in Norway, i.e., TV 2, following the A/B testing methodology. We deployed an effective mitigation approach for popularity bias, called Calibrated Popularity (CP), and monitored its performance in comparison to the platform’s existing collaborative filtering recommendation approach as a baseline over a period of almost four months. The results obtained from a large user base interacting in real-time with the recommendations indicate that the evaluated debiasing approach can be effective in addressing popularity bias while still maintaining the level of user interest and engagement.

The workshop aims to discuss open problems, challenges, and innovative research approaches in the area of persuasive and behavior change recommender systems, that is, recommender systems aimed at modifying people's habits and behavior. Some questions that motivate this workshop are: What kind of theory is more suitable to inform the design of behavior change recommender systems? What kind of personal data (e.g., coming from environmental sensors, wearable devices, etc.) should we use to design behavior change recommendations? How should we deliver them (i.e., what kind of communication channels and interfaces should we use)? What kind of strategies should we implement to design timely and contextualized recommendations? How can we support the user's motivation to adhere to the recommendations provided? How can we “persuade” users in the long term?

Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.

Multi-list recommender systems have become widespread in entertainment and e-commerce applications. Yet, extensive user evaluation research is missing. Since most content is optimized toward a user’s current preferences, this may be problematic in recommender domains that involve behavioral change, such as food recommender systems for healthier food intake. We investigate the merits of multi-list recommendation in the context of internet-sourced recipes. We compile lists that adhere to varying food goals in a multi-list interface, examining whether multi-list interfaces and personalized explanations support healthier food choices. We examine the user evaluation (i.e., diversity, understandability, choice difficulty and satisfaction) of a multi-list recommender interface, linking choice behavior to evaluation aspects through the user experience framework. We present two studies, based on (1) similar-item retrieval and (2) knowledge-based recommendation. Study 1 ( N = 366) compared single-list (5 recipes) and multi-list recommenders (25 recipes; presented with or without explanations). Study 2 ( N = 164) compared single-list and multi-list food recommenders with similar set sizes and varied whether presented explanations were personalized. Multi-list interfaces were perceived as more diverse and understandable than single-list interfaces, while results for choice difficulty and satisfaction were mixed. Moreover, multi-list interfaces triggered changes in food choices, which tended to be unhealthier, but also more goal based.

Food recommender systems optimize towards a user’s current preferences. However, appetites may vary, in the sense that users might seek healthy recipes today and look for unhealthy meals tomorrow. In this paper, we propose a novel approach in the food domain to diversify recommendations across different lists to ‘serve’ different users goals, compiled in a multi-list food recommender interface. We evaluated our interface in a 2 (single list vs multiple lists) x 2 (without or with explanations) between-subject user study (N = 366), linking choice behavior and evaluation aspects through the user experience framework. Our multi-list interface was evaluated more favorably than a single-list interface, in terms of diversity and choice satisfaction. Moreover, it triggered changes in food choices, even though these choices were less healthy than those made in the single-list interface.

Abstract Nowadays, a vast amount of clinical data scattered across different sites on the Internet hinders users from finding helpful information for their well-being improvement. Besides, the overload of medical information (e.g., on drugs, medical tests, and treatment suggestions) have brought many difficulties to medical professionals in making patient-oriented decisions. These issues raise the need to apply recommender systems in the healthcare domain to help both, end-users and medical professionals, make more efficient and accurate health-related decisions. In this article, we provide a systematic overview of existing research on healthcare recommender systems. Different from existing related overview papers, our article provides insights into recommendation scenarios and recommendation approaches. Examples thereof are food recommendation, drug recommendation, health status prediction, healthcare service recommendation, and healthcare professional recommendation. Additionally, we develop working examples to give a deep understanding of recommendation algorithms. Finally, we discuss challenges concerning the development of healthcare recommender systems in the future.

HealthRecSys 2020 was the 5th International Workshop on Health Recommender Systems held in conjunction with the 14th ACM Conference on Recommender Systems. This workshop followed the previous workshop in 2019 [4] and focused on the application and potentials of recommender systems on health promotion, health care, and health-related topics. By engaging in the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure. This year, in particular, COVID-19-related contributions were discussed.

HealthRecSys 2019 was the 4th International Workshop on Health Recommender Systems held in conjunction with the 2019 ACM Conference on Recommender Systems in Copenhagen, Denmark. This workshop followed on from of the previous workshop in 2018 [4] and focused on the application and potentials of recommender systems on health promotion, health care and health-related topics. By engaging the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure.

The 3rd International Workshop on Health Recommender Systems was held in conjunction with the 2018 ACM Conference on Recommender Systems in Vancouver, Canada. Following the two prior workshops in 2016 [4] and 2017 [2], the focus of this workshop is to deepen the discussion on health promotion, health care as well as health related methods. This workshop also aims to strengthen the HealthRecSys community, to engage representatives of other health domains into cross-domain collaborations, and to exchange and share infrastructure.

The ACM RecSys'18 Late-Breaking Results track (previously known as the Poster track) is part of the main program of the 2018 ACM Conference on Recommender Systems in Vancouver, Canada. The track attracted 48 submissions this year out of which 18 papers could be accepted resulting in an acceptance rated of 37.5%.

The 2017 Workshop on Health Recommender Systems was held in conjunction with the 2017 ACM Conference on Recommender Systems in Como, Italy. Following the fists workshop in 2016, the focus of this workshop was on enhancing the results of the first workshop by elaborating discussions on the topics, attracting scientist from other domains, finding cross-domain collaboration, and establishing shared infrastructures.

By incorporating healthiness into the food recommendation / ranking process we have the potential to improve the eating habits of a growing number of people who use the Internet as a source of food inspiration. In this paper, using insights gained from various data sources, we explore the feasibility of substituting meals that would typically be recommended to users with similar, healthier dishes. First, by analysing a recipe collection sourced from Allrecipes.com, we quantify the potential for finding replacement recipes, which are comparable but have different nutritional characteristics and are nevertheless highly rated by users. Building on this, we present two controlled user studies (n=107, n=111) investigating how people perceive and select recipes. We show participants are unable to reliably identify which recipe contains most fat due to their answers being biased by lack of information, misleading cues and limited nutritional knowledge on their part. By applying machine learning techniques to predict the preferred recipes, good performance can be achieved using low-level image features and recipe meta-data as predictors. Despite not being able to consciously determine which of two recipes contains most fat, on average, participants select the recipe with the most fat as their preference. The importance of image features reveals that recipe choices are often visually driven. A final user study (n=138) investigates to what extent the predictive models can be used to select recipe replacements such that users can be ``nudged'' towards choosing healthier recipes. Our findings have important implications for online food systems.

Food recommenders have the potential to positively influence the eating habits of users. To achieve this, however, we need to understand how healthy recommendations are and the factors which influence this. Focusing on two approaches from the literature (single item and daily meal plan recommendation) and utilizing a large Internet sourced dataset from Allrecipes.com, we show how algorithmic solutions relate to the healthiness of the underlying recipe collection. First, we analyze the healthiness of Allrecipes.com recipes using nutritional standards from the World Health Organisation and the United Kingdom Food Standards Agency. Second, we investigate user interaction patterns and how these relate to the healthiness of recipes. Third, we experiment with both recommendation approaches. Our results indicate that overall the recipes in the collection are quite unhealthy, but this varies across categories on the website. Users in general tend to interact most often with the least healthy recipes. Recommender algorithms tend to score popular items highly and thus on average promote unhealthy items. This can be tempered, however, with simple post-filtering approaches, which we show by experiment are better suited to some algorithms than others. Similarly, we show that the generation of meal plans can dramatically increase the number of healthy options open to users. One of the main findings is, nevertheless, that the utility of both approaches is strongly restricted by the recipe collection. Based on our findings we draw conclusions how researchers should attempt to make food recommendation systems promote healthy nutrition.

The first Workshop on Engendering Health with Recommender Systems was organized in conjunction with ACM RecSys 2016. The focus of the workshop was on bringing together researchers and practitioners from diverse areas of health, well-being, decision support, and behavioral change. Health-related issues in recommender systems have been a growing research topic in the recent years and this was a initial attempt at bringing together academics and practitioners to share their experiences on working on related issues.

In this paper, we present work-in-progress of a recently started project that aims at studying the effect of time in recommender systems in the context of social tagging. Despite the existence of previous work in this area, no research has yet made an extensive evaluation and comparison of time-aware recommendation methods. With this motivation, this paper presents results of a study where we focused on understanding (i) "when" to use the temporal information into traditional collaborative filtering (CF) algorithms, and (ii) "how" to weight the similarity between users and items by exploring the effect of different time-decay functions. As the results of our extensive evaluation conducted over five social tagging systems (Delicious, BibSonomy, CiteULike, MovieLens, and Last.fm) suggest, the step (when) in which time is incorporated in the CF algorithm has substantial effect on accuracy, and the type of time-decay function (how) plays a role on accuracy and coverage mostly under pre-filtering on user-based CF, while item-based shows stronger stability over the experimental conditions.

Large scale virtual worlds such as massive multiplayer online games or 3D worlds gained tremendous popularity over the past few years. With the large and ever increasing amount of content available, virtual world users face the information overload problem. To tackle this issue, game-designers usually deploy recommendation services with the aim of making the virtual world a more joyful environment to be connected at. In this context, we present in this paper the results of a project that aims at understanding the mobility patterns of virtual world users in order to derive place recommenders for helping them to explore content more efficiently. Our study focus on the virtual world SecondLife, one of the largest and most prominent in recent years. Since SecondLife is comparable to real-world Location-based Social Networks (LBSNs), i.e., users can both check-in and share visited virtual places, a natural approach is to assume that place recommenders that are known to work well on real-world LBSNs will also work well on SecondLife. We have put this assumption to the test and found out that (i) while collaborative filtering algorithms have compatible performances in both environments, (ii) existing place recommenders based on geographic metadata are not useful in SecondLife.

No abstract available.

Crowdsourcing has emerged as a way to harvest social wisdom from thousands of volunteers to perform a series of tasks online. However, little research has been devoted to exploring the impact of various factors such as the content of a resource or crowdsourcing interface design on user tagging behavior. Although images' titles and descriptions are frequently available in image digital libraries, it is not clear whether they should be displayed to crowdworkers engaged in tagging. This paper focuses on offering insight to the curators of digital image libraries who face this dilemma by examining (i) how descriptions influence the user in his/her tagging behavior and (ii) how this relates to the (a) nature of the tags, (b) the emergent folksonomy, and (c) the findability of the images in the tagging system. We compared two different methods for collecting image tags from Amazon's Mechanical Turk's crowdworkers—with and without image descriptions. Several properties of generated tags were examined from different perspectives: diversity, specificity, reusability, quality, similarity, descriptiveness, and so on. In addition, the study was carried out to examine the impact of image description on supporting users' information seeking with a tag cloud interface. The results showed that the properties of tags are affected by the crowdsourcing approach. Tags from the “with description” condition are more diverse and more specific than tags from the “without description” condition, while the latter has a higher tag reuse rate. A user study also revealed that different tag sets provided different support for search. Tags produced “with description” shortened the path to the target results, whereas tags produced without description increased user success in the search task.

Existing approaches to predicting tie strength between users involve either online social networks or location-based social networks. To date, few studies combined these networks to investigate the intensity of social relations between users. In this paper we analyzed tie strength defined as partners and acquaintances in two domains: a location-based social network and an online social network (Second Life). We compared user pairs in terms of their partnership and found significant differences between partners and acquaintances. Following these observations, we evaluated the social proximity of users via supervised and unsupervised learning algorithms and established that homophilic features were most valuable for the prediction of partnership.

When interacting with social tagging systems, humans exercise complex processes of categorization that have been the topic of much research in cognitive science. In this paper we present a recommender approach for social tags derived from ALCOVE, a model of human category learning. The basic architecture is a simple three-layers connectionist model. The input layer encodes patterns of semantic features of a user-specific resource, such as latent topics elicited through Latent Dirichlet Allocation (LDA) or available external categories. The hidden layer categorizes the resource by matching the encoded pattern against already learned exemplar patterns. The latter are composed of unique feature patterns and associated tag distributions. Finally, the output layer samples tags from the associated tag distributions to verbalize the preceding categorization process. We have evaluated this approach on a real-world folksonomy gathered from Wikipedia bookmarks in Delicious. In the experiment our approach outperformed LDA, a well-established algorithm. We attribute this to the fact that our approach processes semantic information (either latent topics or external categories) across the three different layers. With this paper, we demonstrate that a theoretically guided design of algorithms not only holds potential for improving existing recommendation mechanisms, but it also allows us to derive more generalizable insights about how human information interaction on the Web is determined by both semantic and verbal processes.

Recently, a number of algorithms have been proposed to obtain hierarchical structures - so-called folksonomies - from social tagging data. Work on these algorithms is in part driven by a belief that folksonomies are useful for tasks such as: (a) Navigating social tagging systems and (b) Acquiring semantic relationships between tags. While the promises and pitfalls of the latter have been studied to some extent, we know very little about the extent to which folksonomies are pragmatically useful for navigating social tagging systems. This paper sets out to address this gap by presenting and applying a pragmatic framework for evaluating folksonomies. We model exploratory navigation of a tagging system as decentralized search on a network of tags. Evaluation is based on the fact that the performance of a decentralized search algorithm depends on the quality of the background knowledge used. The key idea of our approach is to use hierarchical structures learned by folksonomy algorithm as background knowledge for decentralized search. Utilizing decentralized search on tag networks in combination with different folksonomies as hierarchical background knowledge allows us to evaluate navigational tasks in social tagging systems. Our experiments with four state-of-the-art folksonomy algorithms on five different social tagging datasets reveal that existing folksonomy algorithms exhibit significant, previously undiscovered, differences with regard to their utility for navigation. Our results are relevant for engineers aiming to improve navigability of social tagging systems and for scientists aiming to evaluate different folksonomy algorithms from a pragmatic perspective.