Graph-based Recommender System Research Articles

A knowledge graph-based recommender system for dementia care: Design and evaluation study

BackgroundCaring for people with dementia is perceived as one of the most challenging caring roles, so effective and practical support is essential. One such innovative approach to internet-based tailored health intervention is the use of recommender system. ObjectiveThis study develops a dementia care intelligent recommender system (DCIRS) that can provide personalized and timely care recommendations for caregivers when they encounter difficult various care problems in people with dementia. MethodsThe development process was divided into 3 stages. In stage 1, we complete the construction of the domain knowledge graph of dementia care. In stage 2, the established domain knowledge graph of dementia care was introduced into the recommendation model by the way of graph embedding to form a recommendation model composed of graph embedding module and recommendation module. In stage 3, on the basis of the application of knowledge graph and recommendation mode, DCIRS was developed, for practical use. In addition, DCIRS has been validated for accuracy for assessing the correctness of the profiling and recommendation, by enrolling 56 caregivers. ResultsThe proposed DCIRS has functions of knowledge graph management and dementia care decision support. Experiments on 56 caregivers in single class recommendation task; the value of accuracy is equals to 98.92% and indicates the high capability of DCIRS. ConclusionsThis study was a pioneering research to develop a more comprehensive DCIRS for caregivers of people with dementia. According to the evaluation results, our DCIRS showing high specificity and accuracy. This system can provide a novel perspective for patient-centered and needs-based support of caregivers of people with dementia.

Enhancing Biomedical Knowledge Representation Through Knowledge Graphs

There is a plethora of information related to the biomedicaldomain on the internet. Unfortunately, retrieving thisinformation online is challenging because of insufficientsemantic metadata embedded within the web documents thathelp search engines interpret the biomedical information.Semantic annotators have partially bridged this gap, yetthese tools frequently need to catch up in accuracy, speed,and the ability to dynamically represent knowledge. Weinitially developed "Semantically," a biomedical semanticcontent authoring platform to streamline and enhancebiomedical annotations through a social-technical approach.Even so, the current system stores data in a relationalschema, which lacks machine-readable content that allowssearch engines to parse the meanings to annotationrecommendations. There is still the need for theamalgamation and contextually rich representation ofannotation recommendation information to enhancenavigation and exploration of data. Therefore, we propose aknowledge graph-based recommendation system with annlp-enhanced search query to provide an environment foreasy and quick access to optimal recommendations in amachine-readable knowledge graph format. We obtainresults for the knowledge graph through an evaluationsurvey that substantiates the efficacy of our knowledgegraph-based recommendation system, highlighting its role inadvancing dynamic knowledge representation and semanticannotation in the biomedical domain. A demo is available atSebC750/Semantically at Knowledge_Graph_Branch(github.com)

A Graph-Based Recommendation System Leveraging Cosine Similarity for Enhanced Marketing Decisions

A Graph-Based Recommendation System Leveraging Cosine Similarity for Enhanced Marketing Decisions

Dual-view multi-modal contrastive learning for graph-based recommender systems

Personalized recommender systems play a crucial role in various online content-sharing platforms (e.g., TikTok). The learning of representations for multi-modal content is pivotal in current graph-based recommender systems. Existing works aim to enhance recommendation accuracy by leveraging multi-modal features (e.g., image, sound, text) as side information for items. However, this approach falls short in fully discerning users' fine-grained preferences across different modalities. To tackle this limitation, this paper introduces the Dual-view Multi-Modal contrastive learning Recommendation model (DMM-Rec). DMM-Rec employs self-supervised learning to guide the learning of user and item representations within the multi-modal context. Specifically, to capture users' preferences for different modalities, we propose specific-modal contrastive learning. Simultaneously, to capture users' cross-modal preferences, cross-modal contrastive learning is introduced to uncover interdependencies in users' preferences across modalities. The contrastive learning tasks not only adaptively explore potential relations between modalities but also address the data sparsity challenge in recommender systems. Extensive experiments conducted on three datasets and compared against ten baselines demonstrate that DMM-Rec outperforms the strongest baseline by an average of 6.81%. These results underscore the effectiveness of considering multi-modal content in improving recommender systems.

Digital Personal Health Coaching Platform for Promoting Human Papillomavirus Infection Vaccinations and Cancer Prevention: Knowledge Graph-Based Recommendation System.

Health promotion can empower populations to gain more control over their well-being by using digital interventions that focus on preventing the root causes of diseases. Digital platforms for personalized health coaching can improve health literacy and information-seeking behavior, leading to better health outcomes. Personal health records have been designed to enhance patients' self-management of a disease or condition. Existing personal health records have been mostly designed and deployed as a supplementary service that acts as views into electronic health records. We aim to overcome some of the limitations of electronic health records. This study aims to design and develop a personal health library (PHL) that generates personalized recommendations for human papillomavirus (HPV) vaccine promotion and cancer prevention. We have designed a proof-of-concept prototype of the Digital Personal Health Librarian, which leverages machine learning; natural language processing; and several innovative technological infrastructures, including the Semantic Web, social linked data, web application programming interfaces, and hypermedia-based discovery, to generate a personal health knowledge graph. We have designed and implemented a proof-of-the-concept prototype to showcase and demonstrate how the PHL can be used to store an individual's health data, for example, a personal health knowledge graph. This is integrated with web-scale knowledge to support HPV vaccine promotion and prevent HPV-associated cancers among adolescents and their caregivers. We also demonstrated how the Digital Personal Health Librarian uses the PHL to provide evidence-based insights and knowledge-driven explanations that are personalized and inform health decision-making. Digital platforms such as the PHL can be instrumental in improving precision health promotion and education strategies that address population-specific needs (ie, health literacy, digital competency, and language barriers) and empower individuals by facilitating knowledge acquisition to make healthy choices.

Hybrid scientific article recommendation system with COOT optimization

Today, recommendation systems are everywhere, making a variety of activities considerably more manageable. These systems help users by personalizing their suggestions to their interests and needs. They can propose various goods, including music, courses, articles, agricultural products, fertilizers, books, movies, and foods. In the case of research articles, recommendation algorithms play an essential role in minimizing the time required for researchers to find relevant articles. Despite multiple challenges, these systems must solve serious issues such as the cold-start problem, article privacy, and changing user interests. This research addresses these issues through the use of two techniques: hybrid recommendation systems and COOT optimization. To generate article recommendations, a hybrid recommendation system integrates features from content-based and graph-based recommendation systems. COOT optimization is used to optimize the results, inspired by the movement of water birds. The proposed method combines a graph-based recommendation system with COOT optimization to increase accuracy and reduce result inaccuracies. When compared to the baseline approaches described, the model provided in this study improves precision by 2.3%, recall by 1.6%, and mean reciprocal rank (MRR) by 5.7%.

Using Graph Neural Networks for Social Recommendations

Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the recent advances in Graph Neural Networks have led to the prominence of a new family of graph-based recommender system algorithms. In this work, we propose the RelationalNet algorithm, which not only models user–item, and user–user relationships but also item–item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item–item interactions is to enrich the item embeddings by leveraging the similarities between items. By using Graph Neural Networks (GNNs), RelationalNet incorporates social influence and similar item influence into the recommendation process and captures more accurate user interests, especially when traditional methods fall short due to data sparsity. Such models improve the accuracy and effectiveness of recommendation systems by leveraging social connections and item interactions. Results demonstrate that RelationalNet outperforms current state-of-the-art social recommendation algorithms.

Enhanced Multi-Task Learning and Knowledge Graph-Based Recommender System

In recent years, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> ulti-task learning for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> nowledge graph-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</i> ecommender system, termed MKR, has shown its promising performance and has attracted increasing interest, because a recommendation task and a knowledge graph embedding (KGE) task can help each other to improve the recommendation. However, MKR still has two difficult issues. The first is how fully to capture users' historical behavior pattern in the recommendation task and how fully to utilize deep multi-relation semantic information in the KGE task. The second is how to deal with datasets with different sparsity. Tackling these challenging issues, this paper proposes an enhanced MKR (EMKR) approach with two novelties. First, we propose to utilize the attention mechanism to aggregate users' historical behavior for more accurately mining preferences in the recommendation task, and utilize the relation-aware graph convolutional neural network to fully capture the deep multi-relation neighborhood features in the KGE task, so as to address the first issue. Second, a two-part modeling strategy is proposed for a better representation of users in the recommendation task to expand the expressive ability of the model for adapting to datasets with different sparsity, so as to address the second issue. Extensive experiments are conducted on widely-used datasets and 11 approaches are used for comparison. The results show that the proposed EMKR can achieve substantial gains over the compared state-of-the-art approaches, especially in the situation where user-item interactions are sparse.

Usability Evaluation of a Knowledge Graph-Based Dementia Care Intelligent Recommender System: Mixed Methods Study.

Knowledge graph-based recommender systems offer the possibility of meeting the personalized needs of people with dementia and their caregivers. However, the usability of such a recommender system remains unknown. This study aimed to evaluate the usability of a knowledge graph-based dementia care intelligent recommender system (DCIRS). We used a convergent mixed methods design to conduct the usability evaluation, including the collection of quantitative and qualitative data. Participants were recruited through social media advertisements. After 2 weeks of DCIRS use, feedback was collected with the Computer System Usability Questionnaire and semistructured interviews. Descriptive statistics were used to describe sociodemographic characteristics and questionnaire scores. Qualitative data were analyzed systematically using inductive thematic analysis. A total of 56 caregivers were recruited. Quantitative data suggested that the DCIRS was easy for caregivers to use, and the mean questionnaire score was 2.14. Qualitative data showed that caregivers generally believed that the content of the DCIRS was professional, easy to understand, and instructive, and could meet users' personalized needs; they were willing to continue to use it. However, the DCIRS also had some shortcomings. Functions that enable interactions between professionals and caregivers and that provide caregiver support and resource recommendations might be added to improve the system's usability. The recommender system provides a solution to meet the personalized needs of people with dementia and their caregivers and has the potential to substantially improve health outcomes. The next step will be to optimize and update the recommender system based on caregivers' suggestions and evaluate the effect of the application.

Graph-Based Recommendation System Enhanced by Community Detection

Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users’ interests over time this article has considered the time of tag assignments in co-occurrence tags for determining the similarity of tags. Then the graph is created based on similarity of tags. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So, recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been evaluated using two criteria of precision and recall through evaluations on two public datasets. The evaluation results show that the precision and recall of the proposed method have significantly improved, compared to the other methods. According to the experimental results, the criteria of recall and precision have been improved, on average by 5% and 7%, respectively.

KPRLN: deep knowledge preference-aware reinforcement learning network for recommendation

AbstractUser preference information plays an important role in knowledge graph-based recommender systems, which is reflected in users having different preferences for each entity–relation pair in the knowledge graph. Existing approaches have not modeled this fine-grained user preference feature well, as affecting the performance of recommender systems. In this paper, we propose a deep knowledge preference-aware reinforcement learning network (KPRLN) for the recommendation, which builds paths between user’s historical interaction items in the knowledge graph, learns the preference features of each user–entity–relation and generates the weighted knowledge graph with fine-grained preference features. First, we proposed a hierarchical propagation path construction method to address the problems of the pendant entity and long path exploration in the knowledge graph. The method expands outward to form clusters centered on items and uses them to represent the starting and target states in reinforcement learning. With the iteration of clusters, we can better learn the pendant entity preference and explore farther paths. Besides, we design an attention graph convolutional network, which focuses on more influential entity–relation pairs, to aggregate user and item higher order representations that contain fine-grained preference features. Finally, extensive experiments on two real-world datasets demonstrate that our method outperforms other state-of-the-art baselines.

Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding

Recommendation systems are an important and undeniable part of modern systems and applications. Recommending items and users to the users that are likely to buy or interact with them is a modern solution for AI-based applications. In this article, a novel architecture is used with the utilization of pre-trained knowledge graph embeddings of different approaches. The proposed architecture consists of several stages that have various advantages. In the first step of the proposed method, a knowledge graph from data is created, since multi-hop neighbors in this graph address the ambiguity and redundancy problems. Then knowledge graph representation learning techniques are used to learn low-dimensional vector representations for knowledge graph components. In the following a neural collaborative filtering framework is used which benefits from no extra weights on layers. It is only dependent on matrix operations. Learning over these operations uses the pre-trained embeddings, and fine-tune them. Evaluation metrics show that the proposed method is superior in over other state-of-the-art approaches. According to the experimental results, the criteria of recall, precision, and F1-score have been improved, on average by 3.87%, 2.42%, and 6.05%, respectively.

Revisiting Graph-based Recommender Systems from the Perspective of Variational Auto-Encoder

Graph-based recommender system has attracted widespread attention and produced a series of research results. Because of the powerful high-order connection modeling capabilities of the Graph Neural Network, the performance of these graph-based recommender systems are far superior to those of traditional neural network-based collaborative filtering models. However, from both analytical and empirical perspectives, the apparent performance improvement is accompanied with a significant time overhead, which is noticeable in large-scale graph topologies. More importantly, the intrinsic data-sparsity problem substantially limits the performance of graph-based recommender systems, which compelled us to revisit graph-based recommendation from a novel perspective. In this article, we focus on analyzing the time complexity of graph-based recommender systems to make it more suitable for real large-scale application scenarios. We propose a novel end-to-end graph recommendation model called the Collaborative Variational Graph Auto-Encoder (CVGA), which uses the information propagation and aggregation paradigms to encode user–item collaborative relationships on the user–item interaction bipartite graph. These relationships are utilized to infer the probability distribution of user behavior for parameter estimation rather than learning user or item embeddings. By doing so, we reconstruct the whole user–item interaction graph according to the known probability distribution in a feasible and elegant manner. From the perspective of the graph auto-encoder, we convert the graph recommendation task into a graph generation problem and are able to do it with approximately linear time complexity. Extensive experiments on four real-world benchmark datasets demonstrate that CVGA can be trained at a faster speed while maintaining comparable performance over state-of-the-art baselines for graph-based recommendation tasks. Further analysis shows that CVGA can effectively mitigate the data sparsity problem and performs equally well on large-scale datasets.

The application of health recommender systems based on knowledge graph: a scoping review

Abstract Background: Tailored knowledge graph-based recommender systems (KGRSs) have been demonstrated to be able to provide accurate and effective health recommendations to users, and thus significantly reduce health care costs. They are now strongly recommended to be applied in the health care field. Objective: This scoping review aims to identify the current application of KGRSs, their target users and performance metrics, and the potential limitations of implementing health recommender systems in clinical practice. Methods: A review of the studies published from inception to November 1, 2022 was conducted, using key search terms in 6 scientific databases to identify health recommender systems based on knowledge graph technology. Key information from the included studies was extracted and charted. The scoping review was reported following the PRISMA Extension for Scoping Reviews. Result: We included 16 studies and 5 grants totally about the health recommender systems based on knowledge graph technology. They were used in different health areas: traditional Chinese medicine, health management, disease-related decision support, diet, and nutrition recommendations. Among them, 6 studies were for the general public and 6 were for physicians. A total of 13 (81.25%) studies evaluated the KGRS using performance metrics, such as accuracy, recall, F1 score, and area under the curve. All studies pointed out the limitations of the recommender systems and provided directions for their subsequent optimization and improvement. Conclusion: This review describes the state-of-the-art and potential limitations of KGRS used in the health care field. This novel approach has been proven to be effective in overcoming the drawbacks of traditional algorithms, helping users filter massive amounts of data to find out the personalized information they need. Its great potential in digital health needs to be further explored.

Job Seeker Recommendation for Employers: A Graph-Based Recommendation Approach Using Node Embedding

Through the growth and development of online recruitment platforms, thousands of individuals can see vacancies promoted by companies. The fact that hundreds of job seekers are applying for a single position prolongs the hiring process for companies and increases the need for human resources to review these applications. In this study, a graph-based recommendation system is suggested as a solution to the issue that many companies are now experiencing. Node embedding and feature representation methods are explored and the link prediction successes of these methods are examined, by associating the candidates that applied for the same jobs in a graph structure.

Interdisciplinary IoT and Emotion Knowledge Graph-Based Recommendation System to Boost Mental Health

Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health.

A Survey on Knowledge Graph-Based Recommender Systems

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.

A Systematic Review of Deep Knowledge Graph-Based Recommender Systems, with Focus on Explainable Embeddings

Recommender systems (RS) have been developed to make personalized suggestions and enrich users’ preferences in various online applications to address the information explosion problems. However, traditional recommender-based systems act as black boxes, not presenting the user with insights into the system logic or reasons for recommendations. Recently, generating explainable recommendations with deep knowledge graphs (DKG) has attracted significant attention. DKG is a subset of explainable artificial intelligence (XAI) that utilizes the strengths of deep learning (DL) algorithms to learn, provide high-quality predictions, and complement the weaknesses of knowledge graphs (KGs) in the explainability of recommendations. DKG-based models can provide more meaningful, insightful, and trustworthy justifications for recommended items and alleviate the information explosion problems. Although several studies have been carried out on RS, only a few papers have been published on DKG-based methodologies, and a review in this new research direction is still insufficiently explored. To fill this literature gap, this paper uses a systematic literature review framework to survey the recently published papers from 2018 to 2022 in the landscape of DKG and XAI. We analyze how the methods produced in these papers extract essential information from graph-based representations to improve recommendations’ accuracy, explainability, and reliability. From the perspective of the leveraged knowledge-graph related information and how the knowledge-graph or path embeddings are learned and integrated with the DL methods, we carefully select and classify these published works into four main categories: the Two-stage explainable learning methods, the Joint-stage explainable learning methods, the Path-embedding explainable learning methods, and the Propagation explainable learning methods. We further summarize these works according to the characteristics of the approaches and the recommendation scenarios to facilitate the ease of checking the literature. We finally conclude by discussing some open challenges left for future research in this vibrant field.

SGCF: Inductive Movie Recommendation System with Strongly Connected Neighborhood Sampling

User and item embeddings are key resources for the development of recommender systems. Recent works has exploited connectivity between users and items in graphs to incorporate the preferences of local neighborhoods into embeddings. Information inferred from graph connections is very useful, especially when interaction between user and item is sparse. In this paper, we propose graphSAGE Collaborative Filtering (SGCF), an inductive graph-based recommendation system with local sampling weight. We conducted an experiment to investigate recommendation performance for SGCF by comparing its performance with baseline and several SGCF variants in Movielens dataset, which are commonly used as recommendation system benchmark data. Our experiment shows that weighted SGCF perform 0.5% higher than benchmark in NDCG@5 and NDCG@10, and 0.8% in NDCG@100. Weighted SGCF perform 0.79% higher than benchmark in recall@5, 0.4% increase for recall@10 and 1.85% increase for recall@100. All the improvements are statistically significant with p-value 0.05.

Cross-domain knowledge graph chiasmal embedding for multi-domain item-item recommendation

Recommender system can provide users with the required information accurately and efficiently, playing a very important role in improving users' life experience. Although knowledge graph-based recommender system can solve the sparsity and cold start problems faced by traditional recommender system, it cannot handle the cross-domain cold start problem and cannot provide multi-domain recommendations. Therefore, this paper focuses on multi-domain item-item (I2I) recommendation based on cross-domain knowledge graph embedding by analyzing the association between items of the same domain and the interaction between items of diverse domains with the aid of knowledge graph that contains rich information. Firstly, a cross-domain knowledge graph chiasmal embedding approach is proposed to efficiently interact all items in multiple domains. To help achieve both homo-domain embedding and hetero-domain embedding of items, a binding rule is put forward. Secondly, a multi-domain I2I recommendation method is presented to efficiently recommend items in multiple domains, which is a recommendation method based on link prediction of knowledge graph. Finally, the proposed methods are compared and analyzed with some benchmark methods using two datasets. The experimental results show that the proposed methods achieve better link prediction results and multi-domain recommendation results.