CareGraph: A Graph-based Recommender System for Diabetes Self-Care

1 INTRODUCTION . The recent global pandemic has brought with it a permanent shift away from traditional in-person health consultations , towards large digital telehealth platforms that support remote consults . This research is performed in the context of one such platform that provides a mobile application to help people manage chronic diseases such as diabetes , hypertension and obesity . As solution designers , some of the key problems we face is to get our users ’ attention , foster awareness and encourage them to take actions that help manage their chronic health conditions . Sending notifications that nudge users is one efficient way to encourage engagement . However , user preferences for engaging with these nudges can vary greatly , different users require different persuasion techniques . Even within a homogeneous messaging context , different tones in messages can appeal to different users . Recommendation systems can be applied to model preference patterns and predict effective personalized nudge notifications . Collaborative recommender systems are well established tools for predicting user preference and have been shown to perform very well , provided there is sufficient information available to model the users preferences , as highlighted by seminal papers in the field such as Resnick et al . ( 1994 ) . For new or esoteric items , or users , there is frequently a lack of sufficient information to make a good prediction . These conditions , known in the literature as ’ cold start ’ are the focus of our experiments in this paper . Specifically in the context of recommending health nudges , which are created by experts who inherently follow guidelines set by associations such as American Diabetes Association ame ( 2021 ) . Our primary research question asks whether a knowledge graph can be applied to mitigate cold start problems , in the specific task of recommending a finite set of highly structured health nudge messages . 1.1 KNOWLEDGE GRAPH-BASED RECOMMENDATION . Online content and services have undergone a huge volume of growth in recent years . Accordingly , recommender systems are increasingly relied upon to help people get to the right information at the right time and also in the right way [ Ricci et al . ( 2011 ) ] . They help customers shorten their times exploring products or services in various applications such as news portals [ Wang et al . ( 2018b ) ] , E-commerce [ Zhang & Jiao ( 2007 ) , Hwangbo et al . ( 2018 ) ] , accommodations [ Haldar et al . ( 2019 ) ] , or music recommendation [ Van Den Oord et al . ( 2013 ) ] . However , our problems are different from these recommendation applications . First , in other applications like E-commerce , users can see a list of products and select their interested items . They can visit websites to search for items of interests anytime they like . In our nudge notifications , our users can just see one nudge at a time , and hence we can not flush a lot of nudges as it will disturb our users and can negatively impact their level of engagement . Hence , recommending the nudges that match user preferences while also ensuring that there is a diversity in the actions we are recommending , and achieving good performance in as few notifications as possible is a crucial requirement in this setting . Also , we frequently need to support the creation of new nudges . Within this setting , using embeddings from supervised learning methodologies may not be able to solve our problems . To learn efficient embeddings from those approaches , we require a lot of data for each item . Organizing and aggregating nudge attributes such as themes , tones or objectives can help the cold-start problem as we can transfer the knowledge of user preferences on nudge attributes to new nudges and help us understand and optimize to user behaviors quickly . For example , if a user has positive responses to encouragement-tone nudges , we can send more encouragement-tone based messages to this user . Knowledge graphs ( KG ) have been applied in various tasks such as search engines [ Yang et al . ( 2017 ) ] , text classification [ Hu et al . ( 2021 ) ] and word embedding [ Celikyilmaz et al . ( 2015 ) ] . They have also been previously introduced to recommendation systems to improve the precision of recommended items and the explainability [ Wang et al . ( 2019 ) ] . A knowledge graph is a directed heterogeneous graph representing real-world objects and relationship among these objects . Nodes in a KG represent entities , which can be items or the attributes that describe the item . The edges represent relations between each entity . Knowledge graphs give recommendation systems the benefit of enriching the semantic relationships among items [ Wang et al . ( 2019 ) ] . When we know users ’ interests in a KG graph , we can extend the diversity of recommended items from the node connections present in the graph . Our problem domain is selecting nudge notifications for our mobile applications that are personalized to individual users preferences . We have different types of nudge notifications such as alerting users in regards to their health condition , reminding users to follow their health routine , providing some useful education related to their health , or introducing new services and events to users . We want to personalize nudges to each user to increase their engagement and reduce over-sending nudges that might disturb the users . The contributions of this work are summarized as follows : • We propose a new method using a neural network that combines user attributes with a knowledge graph to overcome the cold-start problem . • We conduct experiments demonstrating the effectiveness of our new approach , particularly under cold-start conditions . 1.2 DIGITAL HEALTH SYSTEM FOR DIABETES SELF-CARE . This work is based on a system that leverages a Blood-Glucose ( BG ) monitoring device , and a Mobile App that is used to provide useful information and suggest actions to users that are relevant and helpful for managing Diabetes . As a part of this system , users are provided with short text messages , about 180 characters long . The text is designed to nudge a user to specific actions that are relevant to managing the condition , such as , healthy recipes ideas , articles that explain and motivate users to add exercise to their routine , reminders to check and monitor BG levels , talking to expert coaches etc . The short text is accompanied with two buttons , one of which is used to guide the target user to perform a specific action ( e.g . read content , schedule a call with a coach ) , and the other can be used to dismiss the recommendation . In this work we call these recommendations on the Mobile-App , Mobile Nudges ( or Nudges for short ) . 2 RELATED WORK . 2.1 RECOMMENDER SYSTEMS IN DIGITAL HEALTH . Over the past decade , a rapidly increasing volume of digital information to support clinical decision making has become available to be leveraged by healthcare recommender systems . Automated health recommender systems have been deployed in various domains . Some applications aim for improving lifestyle to be healthier through diet [ Elsweiler et al . ( 2017 ) , Achananuparp & Weber ( 2016 ) ] or physical activity recommendations [ Dharia et al . ( 2016 ) ] . One such system , known as Pro-fit [ Dharia et al . ( 2016 ) ] applies a hybrid approach to personalize workout sessions based on a user ’ s contextual data and calendar events . Achananuparp & Weber ( 2016 ) proposed a healthier food substitute suggestion system by introducing a food-context matrix and applying Singular Value Decomposition to get the low-dimensional representation of each food item . The similarity between two food items is measured by cosine similarity . Narducci et al . ( 2015 ) introduced an HealthNet to personalize doctors and hospitals to users , given the user profile and the health data shared by the community . The iDoctor system in Zhang et al . ( 2017b ) provides doctor recommendations by using sentiment analysis from their rating and reviews and Latent Dirichlet Allocation for user preference and doctor features . The hybrid matrix factorization is applied to predict the doctor rating . Recommendation systems have also been used for optimizing treatment plan such as drug recommendation [ Zhang et al . ( 2017a ) , Stark et al . ( 2017 ) ] . Zhang et al . ( 2017a ) applied the concept of neighborhood-based method in a drug-drug interaction prediction to help reduce unexpected effects from using multiple drugs . A drug recommendation system for Migraine diseases proposed by Stark et al . ( 2017 ) using a graph database and collaborative filtering approach . 2.2 KNOWLEDGE GRAPH EMBEDDING . Knowledge graph embedding ( KGE ) is a process to transform a knowledge graph into low-dimensional continuous vector space which still preserves the network structure information . The knowledge graph can be represented as a group of triplets , each of which contains two nodes ( items or attributes ) and the relationship between these nodes . With these triples KGE will project all entities and relations into a low dimensional vector space that preserves their graph structure in these vector representations . There are several approaches for building these representations in the KGE setting . Translation distance models such as TransE [ Bordes et al . ( 2013 ) ] , TransH [ Wang et al . ( 2014 ) ] , TransR [ Lin et al . ( 2015 ) ] and semantic matching models such as DistMult [ Yang et al . ( 2014 ) ] are some of the popular methods . 2.3 KNOWLEDGE GRAPH EMBEDDING IN RECOMMENDATION SYSTEMS . Knowledge graphs have been applied to many recommendation applications . Wang et al . ( 2018a ) proposed the RippleNet , an end-to-end recommendation framework that incorporates knowledge graph into recommendation systems . RippleNet extends user interest of items through links in the knowledge graph which help increasing the diversity . Wang et al . ( 2018b ) introduced Deep Knowledge-aware Network ( DKN ) for news recommendation . The key component of DKN is knowledge-aware convolutional neural network ( KCNN ) for incorporating word-level and knowledge-level representations . Zhang et al . ( 2018 ) extends a collaborative filtering framework to learn over the knowledge graph embedding . A user-item graph is used , where each connection between nodes depicts how a user interacted with an item , for example ( buy , also bought , also view ) Another method for using a knowledge graph in a recommenation system is path-based approach , which uses the connection patterns in the knowledge graph to generate the recommendation . Hete-CF [ Luo et al . ( 2014 ) ] , a collaborative filtering recommendation method on Heterogeneous Social Network , which combines different types of meta-paths ( user-user , user-item and item-item ) and calculates the similarity . Yu et al . ( 2014 ) introduced HeteRec which represents the connectivity between users and items with meta path-based latent features . HeteRec recommendation models are further defined into two levels : global ( HeteRec-g ) and personalized ( HeteRec-p ) levels . In the HeteRec-p , users are clustered based on their interests and preferences into subgroups and then learn recommendation models for each user subgroup . 3 PROBLEM FORMULATION . Our nudge recommendation can be seen as a binary classification problem where we try to predict the probability that a user will have a positive engagement with a nudge ( i.e . accepted the action or suggestion provided by the nudge ) . Let U = { u1 , u2 , ... } and V = { v1 , v2 , ... } denote the sets of users and nudges respectively . A user-item interactive matrix is defined as Y = { yu , v|u ∈ U , v ∈ V } where yu , v = { 1 if the user u positively engaged with the nudge v 0 otherwise ( 1 ) We want our model to learn and predict a probability of ui will click on the nudge vj or ŷui , vj = f ( ui , vj ) .

Summary: This paper proposes a graph-based recommender system for diabetes self-care management. The proposed method bases on the knowledge graph embeddings techniques. The proposed method shows better performance compared with two baselines on metric AUC.