Knowledge graphs (KG) have demonstrated significant potential in recommender systems by providing complementary semantic information that is typically absent in user-item interaction graphs (IG). While contrastive learning has emerged as a powerful paradigm for integrating these dual information sources, we identify a critical limitation in existing approaches: current methods fail to effectively balance the contrastive views derived from IG and KG, often resulting in performance degradation compared to using IG alone. To address this fundamental challenge, we propose SimKGCL, a novel contrastive learning framework that introduces a simple yet principled solution -- cross-view, layer-wise fusion between IG and KG representations prior to contrastive learning. This design ensures effective knowledge transfer while maintaining the discriminative power of contrastive objectives. Comprehensive experiments across three real-world benchmarks demonstrate that our approach not only consistently outperforms existing methods but also achieves remarkable efficiency gains. Our code is available through this link: https://figshare.com/articles/conference_contribution/SimKGCL/22783382.

Personalized recommendation has been instrumental in many real applications. Despite the great progress, the underlying multi-scenario characteristics (e.g., users may behave differently under different scenarios) are largely ignored by existing recommender systems. Intuitively, modeling different scenarios properly could significantly improve the recommendation accuracy, and some existing work has explored this direction. However, these work assumes the scenarios are explicitly given, and thus becomes less effective when such information is unavailable. To complicate things further, proper scenario modeling from data is challenging and the recommendation models may easily overfit to some scenarios. In this paper, we propose a multi-scenario learning framework, MUSENET, for personalized recommendation. The key idea of MUSENET is to learn multiple implicit scenarios from the user behaviors, with a careful design inspired by the causal interpretation of recommender systems to avoid the overfitting issue. Additionally, since users' repeat consumptions account for a large part of the user behavior data on many e-commerce platforms, a repeat-aware mechanism is integrated to handle users' repurchase intentions within each scenario. Comprehensive experimental results on both industrial and public datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art methods.

Graph learning-based collaborative filtering (GLCF), which is built upon the message-passing mechanism of graph neural networks (GNNs), has received great recent attention and exhibited superior performance in recommender systems. However, although GNNs can be easily compromised by adversarial attacks as shown by the prior work, little attention has been paid to the vulnerability of GLCF. Questions like can GLCF models be just as easily fooled as GNNs remain largely unexplored. In this article, we propose to study the vulnerability of GLCF. Specifically, we first propose an adversarial attack against CLCF. Considering the unique challenges of attacking GLCF, we propose to adopt the greedy strategy in searching for the local optimal perturbations and design a reasonable attacking utility function to handle the non-differentiable ranking-oriented metrics. Next, we propose a defense to robustify GCLF. The defense is based on the observation that attacks usually introduce suspicious interactions into the graph to manipulate the message-passing process. We then propose to measure the suspicious score of each interaction and further reduce the message weight of suspicious interactions. We also give a theoretical guarantee of its robustness. Experimental results on three benchmark datasets show the effectiveness of both our attack and defense.

Neural graph collaborative filtering has received great recent attention due to its power of encoding the high-order neighborhood via the backbone graph neural networks. However, their robustness against noisy user-item interactions remains largely unexplored. Existing work on robust collaborative filtering mainly improves the robustness by denoising the graph structure, while recent progress in other fields has shown that directly adding adversarial perturbations in the embedding space can significantly improve the model robustness. In this work, we propose to improve the robustness of neural graph collaborative filtering via both denoising in the structure space and perturbing in the embedding space. Specifically, in the structure space, we measure the reliability of interactions and further use it to affect the message propagation process of the backbone graph neural networks; in the embedding space, we add in-distribution perturbations by mimicking the behavior of adversarial attacks and further combine it with contrastive learning to improve the performance. Extensive experiments have been conducted on four benchmark datasets to evaluate the effectiveness and efficiency of the proposed approach. The results demonstrate that the proposed approach outperforms the recent neural graph collaborative filtering methods especially when there are injected noisy interactions in the training data.

Learning fair representations is an essential task to reduce bias in data-oriented decision making. It protects minority subgroups by requiring the learned representations to be independent of sensitive attributes. To achieve independence, the vast majority of the existing work primarily relaxes it to the minimization of the mutual information between sensitive attributes and learned representations. However, direct computation of mutual information is computationally intractable, and various upper bounds currently used either are still intractable or contradict the utility of the learned representations. In this paper, we introduce distance covariance as a new dependence measure into fair representation learning. By observing that sensitive attributes (e.g., gender, race, and age group) are typically categorical, the distance covariance can be converted to a tractable penalty term without contradicting the utility desideratum. Based on the tractable penalty, we propose FairDisCo, a variational method to learn fair representations. Experiments demonstrate that FairDisCo outperforms existing competitors for fair representation learning.

Co-evolving sequences are ubiquitous in a variety of applications, where different sequences are often inherently inter-connected with each other. We refer to such sequences, together with their inherent connections modeled as a structured network, as network of co-evolving sequences (NoCES). Typical NoCES applications include road traffic monitoring, company revenue prediction, motion capture, etc. To date, it remains a daunting challenge to accurately model NoCES due to the coupling between network structure and sequences. In this paper, we propose to modeling \pname\ with the aim of simultaneously capturing both the dynamics and the interplay between network structure and sequences. Specifically, we propose a joint learning framework to alternatively update the network representations and sequence representations as the sequences evolve over time. A unique feature of our framework lies in that it can deal with the case when there are co-evolving sequences on both network nodes and edges. Experimental evaluations on four real datasets demonstrate that the proposed approach (1) outperforms the existing competitors in terms of prediction accuracy, and (2) scales linearly w.r.t. the sequence length and the network size.

Learning node representations in a network has a wide range of applications. Most of the existing work focuses on improving the performance of the learned node representations by designing advanced network embedding models. In contrast to these work, this article aims to provide some understanding of the rationale behind the existing network embedding models, e.g.,whya given embedding algorithm outputs the specific node representations andhowthe resulting node representations relate to the structure of the input network. In particular, we propose to discern the edge influence for two widely-studied classes of network embedding models, i.e., skip-gram based models and graph neural networks. We provide algorithms to effectively and efficiently quantify the edge influence on node representations, and further identify high-influential edges by exploiting the linkage between edge influence and network structure. Experimental evaluations are conducted on real datasets showing that: 1) in terms of quantifying edge influence, the proposed method is significantly faster (up to$2,000\times$) than straightforward methods with little quality loss, and 2) in terms of identifying high-influential edges, the identified edges by the proposed method have a significant impact in the context of downstream prediction task and adversarial attacking.

Attributed network embedding aims to learn low dimensional node representations by combining both the network's topological structure and node attributes. Most of the existing methods either propagate the attributes over the network structure or learn the node representations by an encoder-decoder framework. However, propagation based methods tend to prefer network structure to node attributes, whereas encoder-decoder methods tend to ignore the longer connections beyond the immediate neighbors. In order to address these limitations while enjoying the best of the two worlds, we design cross fusion layers for unsupervised attributed network embedding. Specifically, we first construct two separate views to handle network structure and node attributes, and then design cross fusion layers to allow flexible information exchange and integration between the two views. The key design goals of the cross fusion layers are three-fold: 1) allowing critical information to be propagated along the network structure, 2) encoding the heterogeneity in the local neighborhood of each node during propagation, and 3) incorporating an additional node attribute channel so that the attribute information will not be overshadowed by the structure view. Extensive experiments on three datasets and three downstream tasks demonstrate the effectiveness of the proposed method.

Network embedding aims to automatically learn the node representations in networks. The basic idea of network embedding is to first construct a network to describe the neighborhood context for each node, and then learn the node representations by designing an objective function to preserve certain properties of the constructed context network. The vast majority of the existing methods, explicitly or implicitly, follow a pointwise design principle. That is, the objective can be decomposed into the summation of the certain goodness function over each individual edge of the context network. In this paper, we propose to go beyond such pointwise approaches, and introduce the ranking-oriented design principle for network embedding. The key idea is to decompose the overall objective function into the summation of a goodness function over a set of edges to collectively preserve their relative rankings on the context network. We instantiate the ranking-oriented design principle by two new network embedding algorithms, including a pairwise network embedding method PaWine which optimizes the relative weights of edge pairs, and a listwise method LiWine which optimizes the relative weights of edge lists. Both proposed algorithms bear a linear time complexity, making themselves scalable to large networks. We conduct extensive experimental evaluations on five real datasets with a variety of downstream learning tasks, which demonstrate that the proposed approaches consistently outperform the existing methods.

Network embedding, which learns the low-dimensional representations of nodes, has gained significant research attention. Despite its superior empirical success, often measured by the prediction performance of downstream tasks (e.g., multi-label classification), it is unclear \em why a given embedding algorithm outputs the specific node representations, and \em how the resulting node representations relate to the structure of the input network. In this paper, we propose to discern the edge influence as the first step towards understanding skip-gram basd network embedding methods. For this purpose, we propose an auditing framework Near, whose key part includes two algorithms (Near-add \ and Near-del ) to effectively and efficiently quantify the influence of each edge. Based on the algorithms, we further identify high-influential edges by exploiting the linkage between edge influence and the network structure. Experimental results demonstrate that the proposed algorithms (Near-add \ and Near-del ) are significantly faster (up to $2,000\times$) than straightforward methods with little quality loss. Moreover, the proposed framework can efficiently identify the most influential edges for network embedding in the context of downstream prediction task and adversarial attacking.

Bug tracking systems, which help to track the reported software bugs, have been widely used in software development and maintenance. In these systems, recognizing relevant source files among a large number of source files for a given bug report is a time-consuming and labor-intensive task for software developers. To tackle this problem, information retrieval methods have been widely used to capture either the textual similarities or the semantic similarities between bug reports and source files. However, these two types of similarities are usually considered separately and the historical bug fixings are largely ignored by the existing methods. In this paper, we propose a supervised topic modeling method (STMLOCATOR) for automatically locating the relevant source files for a given bug report. In particular, the proposed model is built upon three key observations. First, supervised modeling can effectively make use of the existing fixing histories. Second, certain words in bug reports tend to appear multiple times in their relevant source files. Third, longer source files tend to have more bugs. By integrating the above three observations, the proposed STMLOCATOR utilizes historical fixings in a supervised way and learns both the textual similarities and semantic similarities between bug reports and source files. We further consider a special type of bug reports with stack-traces in bug reports, and propose a variant of STMLOCATOR to tailor for such bug reports. Experimental evaluations on three real data sets demonstrate that the proposed STMLOCATOR can achieve up to 23.6% improvement in terms of prediction accuracy over its best competitors, and scales linearly with the size of the data. Moreover, the proposed variant further improves STMLOCATOR by up to 76.2% on those bug reports with stack-traces.

Detecting the travel modes such as walking and driving a car is an important task for user behavior understanding as well as transportation planning and management. Existing solutions for this task mainly train a generic classifier for all users although the walking or driving behaviors may differ greatly from one user to another. In this paper, we propose to build a personalized travel mode detection method. In particular, the proposed method can be divided into two stages. First, for a given target user, it applies user similarity computation to borrow data from a set of pre-collected data for transfer learning. Second, it estimates the data distribution in feature space, and uses it to reweight the borrowed data so as to minimize the model loss with respect to the target user. Experimental evaluations on real travel data show that the proposed method outperforms the generic method and the transfer learning method with kernel mean matching in terms of prediction accuracy.

Latent factor models have become a prevalent method in recommender systems, to predict users' preference on items based on the historical user feedback. Most of the existing methods, explicitly or implicitly, are built upon the first-order rating distance principle, which aims to minimize the difference between the estimated and real ratings. In this paper, we generalize such first-order rating distance principle and propose a new latent factor model (HoORaYs) for recommender systems. The core idea of the proposed method is to explore high-order rating distance, which aims to minimize not only (i) the difference between the estimated and real ratings of the same (user, item) pair (i.e., the first-order rating distance), but also (ii) the difference between the estimated and real rating difference of the same user across different items (i.e., the second-order rating distance). We formulate it as a regularized optimization problem, and propose an effective and scalable algorithm to solve it. Our analysis from the geometry and Bayesian perspectives indicate that by exploring the high-order rating distance, it helps to reduce the variance of the estimator, which in turns leads to better generalization performance (e.g., smaller prediction error). We evaluate the proposed method on four real-world data sets, two with explicit user feedback and the other two with implicit user feedback. Experimental results show that the proposed method consistently outperforms the state-of-the-art methods in terms of the prediction accuracy.

In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RAPARE) to learn the latent profiles of cold-start users/items. The centerpiece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RAPARE strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.

Community Question Answering (CQA) sites, such as Stack Overflow and Yahoo! Answers, have become very popular in recent years. These sites contain rich crowdsourcing knowledge contributed by the site users in the form of questions and answers, and these questions and answers can satisfy the information needs of more users. In this article, we aim at predicting the voting scores of questions/answers shortly after they are posted in the CQA sites. To accomplish this task, we identify three key aspects that matter with the voting of a post, i.e., the non-linear relationships between features and output, the question and answer coupling, and the dynamic fashion of data arrivals. A family of algorithms are proposed to model the above three key aspects. Some approximations and extensions are also proposed to scale up the computation. We analyze the proposed algorithms in terms of optimality, correctness, and complexity. Extensive experimental evaluations conducted on two real data sets demonstrate the effectiveness and efficiency of our algorithms.

With the great popularity of mobile devices, the amount of mobile apps has grown at a more dramatic rate than ever expected. A technical challenge is how to recommend suitable apps to mobile users. In this work, we identify and focus on a unique characteristic that exists in mobile app recommendation—that is, an app usually corresponds to multiple release versions. Based on this characteristic, we propose a fine-grain version-aware app recommendation problem. Instead of directly learning the users’ preferences over the apps, we aim to infer the ratings of users on a specific version of an app. However, the user-version rating matrix will be sparser than the corresponding user-app rating matrix, making existing recommendation methods less effective. In view of this, our approach has made two major extensions. First, we leverage the review text that is associated with each rating record; more importantly, we consider two types of version-based correlations. The first type is to capture the temporal correlations between multiple versions within the same app, and the second type of correlation is to capture the aggregation correlations between similar apps. Experimental results on a large dataset demonstrate the superiority of our approach over several competitive methods.

Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.

Measuring node proximity on large scale networks is a fundamental building block in many application domains, ranging from computer vision, e-commerce, social networks, software engineering, disaster management to biology and epidemiology. The state of the art (e.g., random walk based methods) typically assumes the input network is given a priori, with the known network topology and the associated edge weights. A few recent works aim to further infer the optimal edge weights based on the side information. This paper generalizes the challenge in multiple dimensions, aiming to learn optimal networks for node proximity measures. First (optimization scope), our proposed formulation explores a much larger parameter space, so that it is able to simultaneously infer the optimal network topology and the associated edge weights. This is important as a noisy or missing edge could greatly mislead the network node proximity measures. Second (optimization granularity), while all the existing works assume one common optimal network, be it given as the input or learned by the algorithms, exists for all queries, our method performs optimization at a much finer granularity, essentially being able to infer an optimal network that is specific to a given query. Third (optimization efficiency), we carefully design our algorithms with a linear complexity wrt the neighborhood size of the user preference set. We perform extensive empirical evaluations on a diverse set of 10+ real networks, which show that the proposed algorithms (1) consistently outperform the existing methods on all six commonly used metrics; (2) empirically scale sub-linearly to billion-scale networks and (3) respond in a fraction of a second.

Community Question Answering (CQA) sites have become valuable repositories that host a massive volume of human knowledge. How can we detect a high-value answer which clears the doubts of many users? Can we tell the user if the question s/he is posting would attract a good answer? In this paper, we aim to answer these questions from the perspective of the voting outcome by the site users. Our key observation is that the voting score of an answer is strongly positively correlated with that of its question, and such correlation could be in turn used to boost the prediction performance. Armed with this observation, we propose a family of algorithms to jointly predict the voting scores of questions and answers soon after they are posted in the CQA sites. Experimental evaluations demonstrate the effectiveness of our approaches.

Collaborative filtering is a fundamental building block in many recommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples.

Community Question Answering (CQA) sites have become valuable platforms to create, share, and seek a massive volume of human knowledge. How can we spot an insightful question that would inspire massive further discussions in CQA sites? How can we detect a valuable answer that benefits many users? The long-term impact (e.g., the size of the population a post benefits) of a question/answer post is the key quantity to answer these questions. In this paper, we aim to predict the long-term impact of questions/answers shortly after they are posted in the CQA sites. In particular, we propose a family of algorithms for the prediction problem by modeling three key aspects, i.e., non-linearity, question/answer coupling, and dynamics. We analyze our algorithms in terms of optimality, correctness, and complexity. We conduct extensive experimental evaluations on two real CQA data sets to demonstrate the effectiveness and efficiency of our algorithms.

Inferring the pair-wise trust relationship is a core building block for many real applications. State-of-the-art approaches for such trust inference mainly employ the transitivity property of trust by propagating trust along connected users, but largely ignore other important properties such as trust bias, multi-aspect, etc. In this paper, we propose a new trust inference model to integrate all these important properties. To apply the model to both binary and continuous inference scenarios, we further propose a family of effective and efficient algorithms. Extensive experimental evaluations on real data sets show that our method achieves significant improvement over several existing benchmark approaches, for both quantifying numerical trustworthiness scores and predicting binary trust/distrust signs. In addition, it enjoys linear scalability in both time and space.

Trust inference, which is the mechanism to build new pair-wise trustworthiness relationship based on the existing ones, is a fundamental integral part in many real applications, e.g., e-commerce, social networks, peer-to-peer networks, etc. State-of-the-art trust inference approaches mainly employ the transitivity property of trust by propagating trust along connected users (a.k.a. trust propagation), but largely ignore other important properties, e.g., prior knowledge, multi-aspect, etc.

Trust inference is an essential task in many real world applications. Most of the existing inference algorithms suffer from the scalability issue, making themselves computationally costly, or even infeasible, for the graphs with more than thousands of nodes. In addition, the inference result, which is typically an abstract, numerical trustworthiness score, might be difficult for the end-user to interpret. In this paper, we propose sub graph extraction to address these challenges. The core of the proposed method consists of two stages: path selection and component induction. The outputs of both stages can be used as an intermediate step to speed up a variety of existing trust inference algorithms. Our experimental evaluations on real graphs show that the proposed method can accelerate existing trust inference algorithms, while maintaining high accuracy. In addition, the extracted sub graph provides an intuitive way to interpret the resulting trustworthiness score.