It has been shown that deep recommendation models are susceptible to adversarial attacks, with this vulnerability potentially leading to significant economic losses in the e-commerce field. However, the robustness of deep recommendation models in response to adversarial attacks has not been systematically investigated. In this article, therefore, we comprehensively evaluate the adversarial robustness of various representative deep models in different settings, aiming to analyze their performance impact under adversarial attacks and compare it with traditional collaborative filtering models. Notably, we examine poisoning attacks under different proportions of fake users and various popularity conditions to understand why certain deep recommendation models perform exceptionally or sub-optimally. On this basis, we further proposed practical robustness improvement strategy for the problems found in the evaluation and fully verified it through rigorous experiments. Key findings include: (1) the sparser the training dataset, the weaker the robustness of a recommendation model’s performance under adversarial attacks; (2) deep recommendation models exhibit greater robustness in recommending popular items under adversarial attacks, while they are more vulnerable when attacked with non-popular items; (3) the robustness of deep recommendation models is not consistently weaker than that of traditional collaborative filtering models across all attack settings. These findings highlight the security concerns in deep recommendation systems and contribute to developing more reliable models.

Feature selection is an essential pre-process component in data mining that aims to select the most relevant features from the target dataset. Datasets are always dynamic in real-world applications, and features may exist in stream mode. Then, online streaming feature selection methods are proposed, which deal with streaming features arriving continuously in real-time. However, most existing algorithms prioritize high accuracy and low time-consumption but overlook the stability of the selected features. Stable feature selection results are crucial for users in practice. For instance, in the medical field, unstable feature selection results can make it challenging for experts to identify the main causative factors of a disease. Motivated by this, this article proposes a new online stable streaming feature selection method via feature aggregation named OSSFS. Specifically, inspired by the cohesive MeanShift approach, OSSFS applies an incremental aggregation strategy to partition the streaming features into multiple hyperellipsoids. Then, we incrementally update and merge these hyperellipsoids with new streaming features. Finally, we select representative features from each hyperellipsoid as the final selected feature subset. Extensive experiments are conducted on several real-world datasets to compare our new method with state-of-the-art competing algorithms in cases of stability and predictive accuracy. Experimental results indicate that OSSFS achieves optimal stability without losing prediction accuracy.

The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on the pre-processed node features and do not consider the raw texts. The performance is highly dependent on the choice of the feature pre-processing method. In this paper, we propose P2TAG, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting. P2TAG first pre-trains the language model (LM) and graph neural network (GNN) on TAGs with self-supervised loss. To fully utilize the ability of language models, we adapt the masked language modeling objective for our framework. The pre-trained model is then used for the few-shot node classification with a mixed prompt method, which simultaneously considers both text and graph information. We conduct experiments on six real-world TAGs, including paper citation networks and product co-purchasing networks. Experimental results demonstrate that our proposed framework outperforms existing graph few-shot learning methods on these datasets with +18.98% ~ +32.14% improvements.

The recommendation performance of deep collaborative filtering models drops sharply under imperceptible adversarial perturbations. Some methods promote the robustness of recommendation systems by adversarial training. However, these methods only study shallow models and lack the exploration of deep models. Furthermore, the way these methods add adversarial noise to the weight parameters of users and items is not fully applicable to deep collaborative filtering models, because the adversarial noise is not sufficient to fully affect its network structure with multiple hidden layers. In this article, we propose a novel adversarial training framework, Random Layer-wise Adversarial Training (RAT), which trains a robust deep collaborative filtering model via adversarial noise propagation. Specifically, we inject adversarial noise into the output of the hidden layer in a random layer-wise manner. The adversarial noise propagates forward from the injected position to obtain more flexible model parameters during the adversarial training process. We validate the effectiveness of RAT on multilayer perceptron (MLP) and implement RAT on MLP-based and convolutional neural networks-based deep collaborative filtering models. Experiments on three publicly available datasets show that the deep collaborative filtering model trained by RAT not only defends against adversarial noise but also guarantees recommendation performance.

Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, benefits plenty of practical applications. However, how to do representation learning on the network quickly and effectively is a meaningful and challenging task, especially for the attributed networks. In this paper, we propose HANE, a Hierarchical Attributed Network Embedding framework, which is a fast and effective method by quickly constructing a hierarchical attributed network of different granularities to learn nodes representations. Specifically, for an attributed network, HANE first builds a hierarchy of successively smaller attributed network from fine to coarse by the fast granulation strategy fusing topological structure and node attributes. After using any unsupervised network embedding method to learn nodes representations of the coarsest network, HANE refines the nodes representations of the hierarchical attributed network from coarse to fine. HANE improves the speed of network representation learning while maintaining its performance and the representation learning method of the coarsest network is flexible. We conduct extensive evaluations for the proposed framework HANE on six datasets and two benchmark applications. Experimental results demonstrate that HANE achieves significant improvements over previous state-of-the-art network embedding methods in efficiency and effectiveness.

Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, such as structural similarity, attribute similarity, etc. The low-dimensional vector of the node can be used as the input of the machine learning algorithm and applied to a lot of downstream tasks, such as node classification and link prediction, benefits plenty of practical applications.

Feature selection is one of the core concepts in machine learning, which hugely impacts the model’s performance. For some real-world applications, features may exist in a stream mode that arrives one by one over time, while we cannot know the exact number of features before learning. Online streaming feature selection aims at selecting optimal stream features at each timestamp on the fly. Without the global information of the entire feature space, most of the existing methods select stream features in terms of individual feature information or the comparison of features in pairs. This article proposes a new online scalable streaming feature selection framework from the dynamic decision perspective that is scalable on running time and selected features by dynamic threshold adjustment. Regarding the philosophy of “Thinking-in-Threes”, we classify each new arrival feature as selecting, discarding, or delaying, aiming at minimizing the overall decision risks. With the dynamic updating of global statistical information, we add the selecting features into the candidate feature subset, ignore the discarding features, cache the delaying features into the undetermined feature subset, and wait for more information. Meanwhile, we perform the redundancy analysis for the candidate features and uncertainty analysis for the undetermined features. Extensive experiments on eleven real-world datasets demonstrate the efficiency and scalability of our new framework compared with state-of-the-art algorithms.

Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search. Despite considerable research, a critical issue that has not been systematically studied isdisambiguation on the fly— to complete the disambiguation in the real-time. This is very challenging, as the disambiguation algorithm must be accurate, efficient, and error tolerance. In this paper, we propose a novel framework — CONNA — to train a matching component and a decision component jointly via reinforcement learning. The matching component is responsible for finding the top matched candidate for the given paper, and the decision component is responsible for deciding on assigning the top matched person or creating a new person. The two components are intertwined and can be bootstrapped via jointly training. Empirically, we evaluate CONNA on two name disambiguation datasets. Experimental results show that the proposed framework can achieve a 1.21-19.84 percent improvement on F1-score using joint training of the matching and the decision components. The proposed CONNA has been successfully deployed on AMiner — a large online academic search system.

The paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. It aims to recommend appropriate experts in a discipline to comment on the quality of papers of others in that discipline. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. Generally, the relationship between a paper and a reviewer often depends on the semantic expressions of them. Creating a more expressive representation can make the peer-review process more robust and less arbitrary. So the representations of a paper and a reviewer are very important for the paper-reviewer recommendation. Actually, a reviewer or a paper often belongs to multiple research fields, which increases difficulty in paper-reviewer recommendation. In this article, we propose a Multi-Label Classification method using a HIErarchical and transPArent Representation named Hiepar-MLC . First, we introduce HIErarchical and transPArent Representation (Hiepar) to express the semantic information of the reviewer and the paper. Hiepar is learned from a two-level bidirectional gated recurrent unit based network applying the attention mechanism. It is capable of capturing the two-level hierarchical information (word-sentence-document) and highlighting the elements in reviewers or papers to support the labels. This word-sentence-document information mirrors the hierarchical structure of a reviewer or a paper and captures the exact semantics of them. Then we transform the paper-reviewer recommendation problem into a multi-level classification issue, whose multiple research labels exactly guide the learning process. It is flexible in that we can select any multi-label classification method to solve the paper-reviewer recommendation problem. Further, we propose a simple multi-label-based reviewer assignment (MLBRA) strategy to select the appropriate reviewers. It is interesting in that we also explore the paper-reviewer recommendation in the coarse-grain granularity. Extensive experiments on the real-world dataset consisting of the papers in the ACM Digital Library show that Hiepar-MLC achieves better label prediction performance than the existing representation alternatives. In addition, with the MLBRA strategy, we show the effectiveness and the feasibility of our transformation from paper-reviewer recommendation to multi-label classification.