Cold-Start Recommendation based on Knowledge Graph and Meta-Learning under Positive and Negative sampling

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Knowledge Graph (KG) embedding approaches have been proved effective to infer new facts for a KG based on the existing ones–a problem known as KG completion. However, most of them have focused on static KGs, in fact, relational facts in KGs often show temporal dynamics, e.g., the fact (US, has president, Barack Obama, [2009–2017]) is only valid from 2009 to 2017. Therefore, utilizing available time information to develop temporal KG embedding models is an increasingly important problem. In this paper, we propose a new hyperplane-based time-aware KG embedding model for temporal KG completion. By employing the method of time-specific hyperplanes, our model could explicitly incorporate time information in the entity-relation space to predict missing elements in the KG more effectively, especially temporal scopes for facts with missing time information. Moreover, in order to model and infer four important relation patterns including symmetry, antisymmetry, inversion and composition, we map facts happened at the same time into a polar coordinate system. During training procedure, a time-enhanced negative sampling strategy is proposed to get more effective negative samples. Experimental results on datasets extracted from real-world temporal KGs show that our model significantly outperforms existing state-of-the-art approaches for the KG completion task.

Sparsity is a challenge for knowledge graph completion. The negative sample space, which is much larger than the positive sample space, has many negative samples similar to the positive samples or potential positive samples, which affects the performance of the knowledge graph completion model. We propose a novel knowledge graph completion method, hard negative examples mining bilinear model (HNEMB). This model uses two components to model entities and relations, namely models relations into relation matrices and inverse relation matrices to learn latent inverse semantics of relations, and models entities into head vectors and tail vectors to learn different representations of entity in different positions. Then we calculate the bilinear scores of triples, we choose correct candidates based on the scores. To address the problem of sparsity of the knowledge graph, we first propose a method for mining hard negative examples for knowledge graph completion, to make model effectively capture the associations between head entities, relations and tail entities. Finally, experiments on multiple knowledge graph completion datasets shows that, as compare with baselines, our model achieves significant improvements and shows its advantages to model sparse knowledge graph.

Simple Debiased Contrastive Learning for Sequential Recommendation

Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples --- both informative to model training and reflective of user real needs. In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. Towards this end, we develop a new negative sampling model, Knowledge Graph Policy Network (KGPolicy), which works as a reinforcement learning agent to explore high-quality negatives. Specifically, by conducting our designed exploration operations, it navigates from the target positive interaction, adaptively receives knowledge-aware negative signals, and ultimately yields a potential negative item to train the recommender. We tested on a matrix factorization (MF) model equipped with KGPolicy, and it achieves significant improvements over both state-of-the-art sampling methods like DNS and IRGAN, and KG-enhanced recommender models like KGAT. Further analyses from different angles provide insights of knowledge-aware sampling. We release the codes and datasets at https://github.com/xiangwang1223/kgpolicy.

Live HIV rapid test kits (RTKs) are usually packaged with only a single test buffer vial. A major challenge with HIV testing is the problem of lack of the RTK buffer vial to complete the number of tests in an RTK pack due to misuse, misplacement or contamination of buffer with clients' blood during HIV testing. This has led to the use of sterile normal saline and tap water as substitute liquids in place of the buffer vial. This study compared the use of the commercially provided buffer vial and these substitute liquids in HIV rapid testing while using the commercially provided buffer as gold standard. One hundred (100) HIV positive samples from patients attending antiretroviral therapy (ART) clinic and 100 HIV negative blood samples from blood donors placed in EDTA were tested with Determine RTK using the commercially provided buffer and substitute liquids (Sterile normal saline and tap water). The Determine RTK was also tested using the commercially provided buffer, normal saline and tap water without addition of blood samples. Results of the tests with substitute liquids were compared with those obtained with that of RTK's commercially provided buffer. With HIV positive and negative blood samples, the expected test line and control line or only control line (for negative samples) were observed with the commercially provided buffer and sterile normal saline. When tested without blood samples using the RTK's buffer and normal saline, the control line was observed but no test line for all 100 tests done. Invalid test results were recorded in 48% of the negative samples and 40% of test with no blood added when with tap water was used. This study also shows varied colour intensity for the buffer vial and substitute liquids used. For samples tested with sterile normal saline, 52% of the tested samples showed stronger colour intensity in test line that control line, 37% had equal colour intensity while 11% had higher colour intensity in control than test line. Seventy seven (77%) of Samples tested with tap water had the colour intensity greater in test line than control line while 23% had equal colour intensity in both test and control line. With sterile normal saline used with HIV positive and negative blood samples, a 100% clear background like that of the gold standard was obtained. However, tap water when used as a substitute buffer to run all 100 HIV positive samples had an unclear background. An unclear background was also observed in 100% and 28% of the test ran with tap water only (without blood samples) and with HIV Negative samples respectively.

Knowledge graph (KG) contains well-structured external information and has shown to be effective for high-quality recommendation. However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG. While for model learning, these methods mainly rely on Negative Sampling (NS) to optimize the models for both KG embedding task and recommendation task. Since NS is not robust (e.g., sampling a small fraction of negative instances may lose lots of useful information), it is reasonable to argue that these methods are insufficient to capture collaborative information among users, items, and entities.

Recent studies have shown that the problems of cold processing and data sparsity in recommender systems can be well alleviated with the help of knowledge graph information. Meanwhile, since the information in recommender systems can be represented by graph data structure, graph neural network has great advantages in recommendatory system. Therefore, graph neural network can better mine the deep relationship between objects in the recommender systems which combines with information from knowledge graph. This paper reviews the application of graph neural network in knowledge graph aided recommendation systems. Specifically, this paper firstly sorts out the related knowledge of knowledge graph, recommendation system and graph neural network, including the cold processing and data sparsity, the structure of knowledge graph, and the basic modules of graph neural network. This paper also summarizes the methods such as KGAT and CKAN used in knowledge graph aided recommendation systems in recent years, and gives some discussion on the development of this field.

Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the de-facto standard for knowledge graph completion. Most existing KGE methods suffer from the sparsity challenge, where it is harder to predict entities that appear less frequently in knowledge graphs. In this work, we propose a novel framework KRACL1 to alleviate the widespread sparsity in KGs with graph context and contrastive learning. Firstly, we propose the Knowledge Relational Attention Network (KRAT) to leverage the graph context by simultaneously projecting neighboring triples to different latent spaces and jointly aggregating messages with the attention mechanism. KRAT is capable of capturing the subtle semantic information and importance of different context triples as well as leveraging multi-hop information in knowledge graphs. Secondly, we propose the knowledge contrastive loss by combining the contrastive loss with cross entropy loss, which introduces more negative samples and thus enriches the feedback to sparse entities. Our experiments demonstrate that KRACL achieves superior results across various standard knowledge graph benchmarks, especially on WN18RR and NELL-995 which have large numbers of low in-degree entities. Extensive experiments also bear out KRACL’s effectiveness in handling sparse knowledge graphs and robustness against noisy triples.

Recent progress in personalized recommendation has shown great potential in exploiting structure information provided by a knowledge graph (KG). As a heterogeneous information network, KG contains rich semantic relatedness among entities, which contributes to addressing notorious issues such as data sparsity and cold start. State-of-the-art KG-based recommendation approaches try to propagate information along KG links to encode long-range connectivities into hidden representations. However, most of them only model the user or item representation independently, lacking a focus on user-item interaction. To this end, we propose the Interactive Knowledge Graph Attention Network (IKGAT), which directly models user-item interaction and high-order structure information within KG. For the user representation, following an interactive attention mechanism, we use the item to attend over the user's neighbors and then propagate their information to update the representation. Such a process is extended to multi-hops away to obtain richer neighborhood information. Similarly, the item representation is updated under the supervision of the user. With that design, IKGAT can capture collaborative signals and user preferences effectively. Experiment results on three public datasets show that IKGAT consistently outperforms the state-of-the-art approaches, especially when the dataset is sparse.

The Covid-19 pandemic has not ended yet. Therefore, laboratory tests are very important for determining the diagnosis of Covid-19. At this case, PCR is the gold standard in laboratory tests. Therefore, the accuracy of the PCR assay should be a major concern. This study aims to evaluate the validation results of several Covid-19 PCR reagent kits. A total of 10 positive and negative clinical samples were used as test samples. DNA extraction was performed using a commercial reagent kit. PCR examination was carried out using four different blinded commercial reagent kits. The results of the examination were compared in each sample. The results showed that Reagents kit A, C, and D gave the same results for each sample, namely five positive and five negative samples. Reagent kit B showed different results, namely eight positive samples and two negative samples. A total of 3 (33.3%) of the 10 sample examinations indicated inaccurate results for Reagent kit B. The Reagent kit B test results using two different boxes showed different results with seven positive results by box 1, while eight positive results by another box. In conclusion, 1 out of 4 tested Covid-19 PCR kit reagents gave inaccurate results. Therefore, in covid-19 testing laboratories, it is recommended to choose a PCR reagent kit that has been approved by a trusted organization and perform reagent validation tests.

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning.

Recently, exploiting a knowledge graph (KG) to enrich the semantic representation of a news article have been proven to be effective for news recommendation. These solutions focus on the representation learning for news articles with additional information in the knowledge graph, where the user representations are mainly derived based on these news representations later. However, different users would hold different interests on the same news article. In other words, directly identifying the entities relevant to the user's interest and deriving the resultant user representation could enable a better news recommendation and explanation.

Cross-view contrastive representation learning on meta-path induced graphs with node features for bundle recommendation.

Contrastive learning is an important technique in the field of machine learning for learning data representations. In the field of self-supervised visual representation learning, the strategy of positive and negative sample selection is key to improving the efficiency and effectiveness of model learning. Traditional self-supervised learning methods often employ random sampling strategies to select positive and negative samples, but this approach may result in uneven sample quality when dealing with complex datasets, thereby affecting the learning outcomes. To alleviate this problem, this study is dedicated to exploring more effective strategies for positive and negative sample selection and processing to optimize the self-supervised learning process. To this end, we propose an improved method of self-supervised learning called contrastive learning with enhanced diversity. On the one hand, this method utilizes the weight parameters of the DINO pre-trained model to initialize the feature extraction network of SimCLR, providing more accurate calculations of feature similarity. On the other hand, by setting a threshold on the feature similarity matrix and penalizing (subtracting 0.5 from) similarity scores that do not exceed this threshold, we reduce the excessive impact of high similarity scores on model training, thereby helping the model better distinguish between positive and negative samples. In downstream image classification tasks, we conducted detailed evaluations of the improved model, specifically including fine-tuning and linear evaluation aspects. Experiments show that the proposed approach improves the performance of the loss functions and improves the accuracy of the proposed SimCLR model.