HAGERec: Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation

Dual Preference Distribution Learning for Item Recommendation

Recommender systems can automatically recommend users with items that they probably like. The goal of them is to model the user-item interaction by effectively representing the users and items. Existing methods have primarily learned the user’s preferences and item’s features with vectorized embeddings, and modeled the user’s general preferences to items by the interaction of them. In fact, users have their specific preferences to item attributes and different preferences are usually related. Therefore, exploring the fine-grained preferences as well as modeling the relationships among user’s different preferences could improve the recommendation performance. Toward this end, we propose a dual preference distribution learning framework (DUPLE), which aims to jointly learn a general preference distribution and a specific preference distribution for a given user, where the former corresponds to the user’s general preference to items and the latter refers to the user’s specific preference to item attributes. Notably, the mean vector of each Gaussian distribution can capture the user’s preferences, and the covariance matrix can learn their relationship. Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes. We then can provide the explanation for each recommended item by checking the overlap between its attributes and the user’s preferred attribute profile. Extensive quantitative and qualitative experiments on six public datasets demonstrate the effectiveness and explainability of the DUPLE method.

Knowledge Graph Recommendation Model Based on Adversarial Training

The recommendation model based on the knowledge graph (KG) alleviates the problem of data sparsity in the recommendation to a certain extent and further improves the accuracy, diversity, and interpretability of recommendations. Therefore, the knowledge graph recommendation model has become a major research topic, and the question of how to utilize the entity and relation information fully and effectively in KG has become the focus of research. This paper proposes a knowledge graph recommendation model based on adversarial training (ATKGRM), which can dynamically and adaptively adjust the knowledge graph aggregation weight based on adversarial training to learn the features of users and items more reasonably. First, the generator adopts a novel long- and short-term interest model to obtain user features and item features and generates a high-quality set of candidate items. Then, the discriminator discriminates candidate items by comparing the user’s scores of positive items, negative items, and candidate items. Finally, experimental studies on five real-world datasets with multiple knowledge graph recommendation models and multiple adversarial training recommendation models prove the effectiveness of our model.

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.

Contrastive Learning-Based Dual Dynamic GCN for SAR Image Scene Classification

As a typical label-limited task, it is significant and valuable to explore networks that enable to utilize labeled and unlabeled samples simultaneously for synthetic aperture radar (SAR) image scene classification. Graph convolutional network (GCN) is a powerful semisupervised learning paradigm that helps to capture the topological relationships of scenes in SAR images. While the performance is not satisfactory when existing GCNs are directly used for SAR image scene classification with limited labels, because few methods to characterize the nodes and edges for SAR images. To tackle these issues, we propose a contrastive learning-based dual dynamic GCN (DDGCN) for SAR image scene classification. Specifically, we design a novel contrastive loss to capture the structures of views and scenes, and develop a clustering-based contrastive self-supervised learning model for mapping SAR images from pixel space to high-level embedding space, which facilitates the subsequent node representation and message passing in GCNs. Afterward, we propose a multiple features and parameter sharing dual network framework called DDGCN. One network is a dynamic GCN to keep the local consistency and nonlocal dependency of the same scene with the help of a node attention module and a dynamic correlation matrix learning algorithm. The other is a multiscale and multidirectional fully connected network (FCN) to enlarge the discrepancies between different scenes. Finally, the features obtained by the two branches are fused for classification. A series of experiments on synthetic and real SAR images demonstrate that the proposed method achieves consistently better classification performance than the existing methods.

Leverage knowledge graph and GCN for fine-grained-level clickbait detection

Clickbait is the use of an enticing title as bait to deceive users to click. However, the corresponding content is often disappointing, infuriating or even deceitful. This practice has brought serious damage to our social trust, especially to online media, which is one of the most important channels for information acquisition in our daily life. Currently, clickbait is spreading on the internet and causing serious damage to society. However, research on clickbait detection has not yet been well performed. Almost all existing research treats clickbait detection as a binary classification task and only uses the title as the input. This shallow usage of information and detection technology not only suffers from low performance in real detection (e.g., it is easy to bypass) but is also difficult to use in further research (e.g., potential empirical studies). In this work, we proposed a novel clickbait detection model that incorporated a knowledge graph, a graph convolutional network and a graph attention network to conduct fine-grained-level clickbait detection. According to experiments using a real dataset, our novel proposed model outperformed classical and state-of-the-art baselines. In addition, certain explainability can also be achieved in our model through the graph attention network. Our fine-grained-level results can provide a measurement foundation for future empirical study. To the best of our knowledge, this is the first attempt to incorporate a knowledge graph and deep learning technique to detect clickbait and achieve explainability.

An investigation into the deep learning approach in sentimental analysis using graph-based theories

Sentiment analysis is a branch of natural language analytics that aims to correlate what is expressed which comes normally within unstructured format with what is believed and learnt. Several attempts have tried to address this gap (i.e., Naive Bayes, RNN, LSTM, word embedding, etc.), even though the deep learning models achieved high performance, their generative process remains a "black-box" and not fully disclosed due to the high dimensional feature and the non-deterministic weights assignment. Meanwhile, graphs are becoming more popular when modeling complex systems while being traceable and understood. Here, we reveal that a good trade-off transparency and efficiency could be achieved with a Deep Neural Network by exploring the Credit Assignment Paths theory. To this end, we propose a novel algorithm which alleviates the features' extraction mechanism and attributes an importance level of selected neurons by applying a deterministic edge/node embeddings with attention scores on the input unit and backward path respectively. We experiment on the Twitter Health News dataset were the model has been extended to approach different approximations (tweet/aspect and tweets' source levels, frequency, polarity/subjectivity), it was also transparent and traceable. Moreover, results of comparing with four recent models on same data corpus for tweets analysis showed a rapid convergence with an overall accuracy of ≈83% and 94% of correctly identified true positive sentiments. Therefore, weights can be ideally assigned to specific active features by following the proposed method. As opposite to other compared works, the inferred features are conditioned through the users' preferences (i.e., frequency degree) and via the activation's derivatives (i.e., reject feature if not scored). Future direction will address the inductive aspect of graph embeddings to include dynamic graph structures and expand the model resiliency by considering other datasets like SemEval task7, covid-19 tweets, etc.