Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

Variational recurrent sequence-to-sequence retrieval for stepwise illustration

We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query. This new task extends the traditional cross-modal retrieval, where each image-text pair is treated independently ignoring broader context. We propose a novel variational recurrent seq2seq (VRSS) retrieval model for this seq2seq task. Unlike most cross-modal methods, we generate an image vector corresponding to the latent topic obtained from combining the text semantics and context. This synthetic image embedding point associated with every text embedding point can then be employed for either image generation or image retrieval as desired. We evaluate the model for the application of stepwise illustration of recipes, where a sequence of relevant images are retrieved to best match the steps described in the text. To this end, we build and release a new Stepwise Recipe dataset for research purposes, containing 10K recipes (sequences of image-text pairs) having a total of 67K image-text pairs. To our knowledge, it is the first publicly available dataset to offer rich semantic descriptions in a focused category such as food or recipes. Our model is shown to outperform several competitive and relevant baselines in the experiments. We also provide qualitative analysis of how semantically meaningful the results produced by our model are through human evaluation and comparison with relevant existing methods

Using Consumer Input to Guide the Development of a Nutrition and Health Website

Consumers want timely, research-based information available online. The project objective was to develop a user-friendly nutrition and health website for Colorado Extension consumers. An electronic survey (n=381) was administered to current and potential Extension consumers to understand their: use of the Web and electronic devices; topics of interest; and preferred mode of information delivery. Results, in conjunction with best practices for website usability and health literacy, were used to develop the Live Eat Play Colorado website. Audience-centered websites with content packaged in small doses and delivered via multiple modalities may enhance reach and use of university and Extension resources

Rebel Foods’ Cloud Kitchen Technologies: Food for Thought?

This case study examines the India based cloud kitchens and food services provider Rebel Foods’ technology platforms. We document the development of the company from its foundation in 2004 and the role played by technology in enabling its various lines of business. We describe in detail the technology stack that drives the operations at Rebel Foods. We also present various emerging technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), blockchain and augmented reality (AR) that may be utilized by Rebel Foods to increase efficiency, build customer engagement and improve sales growth and profitability. We critically examine Rebel Foods’ current approach to technology and analyze the various technology options that the company may consider to drive its future strategy

SNAC_OSHC: Exploring a multifaceted approach to develop outside of school hours care as a health promoting setting

Background: Outside of school hours care (OSHC) services are underutilised as health promoting settings, yet are well positioned to influence health behaviours. OSHC Professionals are ideally placed to become positive influencers in this setting, although may require training to confidently perform this role. Aim: This research trialed a multifaceted intervention strategy to increase OSHC Professional’s confidence and competencies, to support a health promoting OSHC environment with a nutrition and PA activity focus. Design and Methods: This exploratory study adopted a mixed methods approach. The three-pronged, multifaceted intervention included: a workshop, a closed Facebook group, and a website. 19 OSHC Professionals, participated in the study and attended a four-hour workshop that addressed health promoting opportunities in OSHC, through training, a closed Facebook group, and a website. Confidence levels, role adequacy and legitimacy were measured pre and post workshop. Interactions on the closed Facebook page was monitored and analysed and four participants undertook exit interviews to discuss their experience of the intervention. Results: Pre workshop 68% of participants had not received any health promotion training for the OSHC setting. Post workshop significant improvements in confidence about menu planning, accessing nutrition information and activities, and use of recipes was observed (P Conclusion: Health promoting competency based training, combined with positive social connections and shared learning experiences, and a website repository improved OSHC Professionals confidence and capacity to provide a health promoting OSHC environment

A multifaceted approach increased staff confidence to develop outside of school hours care as a health promoting setting

Background: Outside-of-school-hours-care (OSHC) services are well positioned to influence the health behaviours of 489, 800 Australian children, and are an important setting for health promotion given the current rates of childhood overweight and obesity and associated health risks. OSHC Professionals are ideally placed to become positive influencers in this setting, although they may require training and support to confidently perform this role. This study piloted a multifaceted intervention strategy to increase OSHC Professional’s confidence and competencies, to support a health promoting OSHC environment with a nutrition and physical activity focus. Methods: A mixed methods approach was used. Nineteen OSHC Professionals participated in the study, including a face-to-face workshop, supported by a closed Facebook group and website. Role adequacy (self-confidence) and legitimacy (professional responsibility) were measured pre and post workshop and evaluated using non-parametric statistics. Facebook interactions were monitored, and four participants undertook qualitative exit interviews to discuss their experiences with the intervention. Results: Pre-workshop 68% of participants had not received any OSHC-specific health promotion training. Post-workshop significant improvements in confidence about menu planning, accessing nutrition information, activities and recipes was observed (P \u3c 0.05 for all). A significant improvement was observed in role support and role related training (P \u3c 0.05). A high level of support and interaction was observed between participants on Facebook and the website was reported a useful repository of information. Conclusions: Health promotion training, combined with positive social connections, shared learning experiences, and a website improved OSHC Professionals confidence and capacity to provide a health promoting OSHC environment. Health promotion professional development for OSHC professionals should be mandated as a minimum requirement, and such learning opportunities should be scaffolded with support available through social media interactions and website access

Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning

Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found online to the appropriate food genres is a challenging task in this domain. Utilizing the knowledge of domain experts to categorize recipes could be a solution. In this study, we present a novel dataset of two million culinary recipes labeled in respective categories leveraging the knowledge of food experts and an active learning technique. To construct the dataset, we collect the recipes from the RecipeNLG dataset. Then, we employ three human experts whose trustworthiness score is higher than 86.667% to categorize 300K recipe by their Named Entity Recognition (NER) and assign it to one of the nine categories: bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides and fusion. Finally, we categorize the remaining 1900K recipes using Active Learning method with a blend of Query-by-Committee and Human In The Loop (HITL) approaches. There are more than two million recipes in our dataset, each of which is categorized and has a confidence score linked with it. For the 9 genres, the Fleiss Kappa score of this massive dataset is roughly 0.56026. We believe that the research community can use this dataset to perform various machine learning tasks such as recipe genre classification, recipe generation of a specific genre, new recipe creation, etc. The dataset can also be used to train and evaluate the performance of various NLP tasks such as named entity recognition, part-of-speech tagging, semantic role labeling, and so on. The dataset will be available upon publication: https://tinyurl.com/3zu4778y

NLP-BASED FOOD SUGGESTIONS SYSTEM – SMART HOMES

With advanced AI, every industry is growing at rocket speed, while the smart home industry has not reached the next-generation level. A home can only be called a real smart home, when it is completely smart and understand what the residents want, and provide service in a timely manner. The residents should live in the house as if they are leaving in a motel while the house itself takes care of itself and give extra benefits to residents like providing food suggestions to the residents for everyday meals based on their taste, culture, weather, type of their food diet, their interest to try new recipes etc. Our system is an NLP Bert model-based similarity prediction model. The system ranks the recipes based on the similarity of the words and context. Recipes have similar ingredients and procedures are considered similar recipes. Overall, the system creates the top K number of recipes based n the number of days' history of eating habits and removes products that are similar to the recent m number of days to make sure the suggestions are not quite repetitive ( here m<<<<n)