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 nding replacement recipes, which are comparable but have di erent 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 nal 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 ndings have important implications for online food systems. KEYWORDSFood RecSys; human decision making; behavioural change; information behaviour ACM Reference format:

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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 algorithms 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.

A government’s response to increasing incidence of lifestyle-related illnesses, such as obesity, has been to encourage people to cook for themselves. The healthiness of home cooking will, nevertheless, depend on what people cook and how they cook it. In this article, one common source of cooking inspiration—Internet-sourced recipes—is investigated in depth. The energy and macronutrient content of 5,237 main meal recipes from the food website are compared with those of 100 main meal recipes from five bestselling cookery books from popular celebrity chefs and 100 ready meals from the three leading UK supermarkets. The comparison is made using nutritional guidelines published by the World Health Organization and the UK Food Standards Agency. The main conclusions drawn from our analyses are that Internet recipes sourced from are less healthy than TV chef recipes and ready meals from leading UK supermarkets. Only 6 out of 5,237 Internet recipes fully complied with the WHO recommendations. Internet recipes were more likely to meet the WHO guidelines for protein than other classes of meal (10.88 v 7% (TV), p < 0.01; 10.86 v 9% (ready), p < 0.01). However, the Internet recipes were less likely to meet the criteria for fat (14.28 v 24 (TV) v 37% (ready); p < 0.01), saturated fat (25.05 v 33 (TV) v 34% (ready); p < 0.01), and fiber (compared to ready meals 16.50 v 56%; p < 0.01). More Internet recipes met the criteria for sodium density than ready meals (19.63 v 4%; p < 0.01), but fewer than the TV chef meals (19.32 v 36%; p < 0.01). For sugar, no differences between Internet recipes and TV chef recipes were observed (81.1 v 81% (TV); p = 0.86), although Internet recipes were less likely to meet the sugar criteria than ready meals (81.1 v 83% (ready); p < 0.01). Repeating the analyses for each year of available data shows that the results are very stable over time.

Workplace learning happens in the process and context of work, is multi-episodic, often informal, problem based and takes place on a just-in-time basis. While this is a very effective means of delivery, it also does not scale very well beyond the immediate context. We review three types of technologies that have been suggested to scale learning and three connected theoretical discourses around learning and its support. Based on these three strands and an in-depth contextual inquiry into two workplace learning domains, health care and building and construction, four design-based research projects were conducted that have given rise to designs for scaling informal learning with technology. The insights gained from the design and contextual inquiry contributed to a model that provides an integrative view on three informal learning processes at work and how they can be supported with technology: (1) task performance, reflection and sensemaking;(2) help seeking, guidance and support; and (3) emergence and maturing of collective knowledge. The model fosters our understanding of how informal learning can be scaled and how an orchestrated set of technologies can support this process.

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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.

Popularity prediction has been studied in diverse online contexts with demonstrable practical, sociological and technical benefit. Here, we add to the popularity prediction literature by studying the popularity of recipes on two large and well visited online recipe portals (Allrecipes.com, USA and Kochbar.de, Germany). Our analyses show differences between the platforms in terms of how the recipes are interacted with and categorized, as well as in the content of the food and its nutritional properties. For both datasets, we were able to show correlations between recipe features and proxies for popularity, which allow popularity of dishes to be predicted with some accuracy. The trends were more prominent in the Kochbar.de dataset, which was mirrored in the results of the prediction task experiments.