Information Theory-Guided Heuristic Progressive Multi-View Coding

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations; the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated; evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely Information theory-guided hierarchical Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods.Comment: This paper is accepted by the jourcal of Neural Networks (Elsevier) by 2023. A revised manuscript of arXiv:2109.0234

M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning

Inspired by the successful application of contrastive learning on graphs, researchers attempt to impose graph contrastive learning approaches on heterogeneous information networks. Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required. Most existing methods for heterogeneous graph contrastive learning are implemented by transforming heterogeneous graphs into homogeneous graphs, which may lead to ramifications that the valuable information carried by non-target nodes is undermined thereby exacerbating the performance of contrastive learning models. Additionally, current heterogeneous graph contrastive learning methods are mainly based on initial meta-paths given by the dataset, yet according to our deep-going exploration, we derive empirical conclusions: only initial meta-paths cannot contain sufficiently discriminative information; and various types of meta-paths can effectively promote the performance of heterogeneous graph contrastive learning methods. To this end, we propose a new multi-scale meta-path integrated heterogeneous graph contrastive learning (M2HGCL) model, which discards the conventional heterogeneity-homogeneity transformation and performs the graph contrastive learning in a joint manner. Specifically, we expand the meta-paths and jointly aggregate the direct neighbor information, the initial meta-path neighbor information and the expanded meta-path neighbor information to sufficiently capture discriminative information. A specific positive sampling strategy is further imposed to remedy the intrinsic deficiency of contrastive learning, i.e., the hard negative sample sampling issue. Through extensive experiments on three real-world datasets, we demonstrate that M2HGCL outperforms the current state-of-the-art baseline models.Comment: Accepted to the conference of ADMA2023 as an Oral presentatio

MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning

As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interference of task-irrelevant information and sample inefficiency, which are related to the recurring existence of trivial constant solutions. From the perspective of dimensional analysis, we find out that the dimensional redundancy and dimensional confounder are the intrinsic issues behind the phenomena, and provide experimental evidence to support our viewpoint. We further propose a simple yet effective approach MetaMask, short for the dimensional Mask learned by Meta-learning, to learn representations against dimensional redundancy and confounder. MetaMask adopts the redundancy-reduction technique to tackle the dimensional redundancy issue and innovatively introduces a dimensional mask to reduce the gradient effects of specific dimensions containing the confounder, which is trained by employing a meta-learning paradigm with the objective of improving the performance of masked representations on a typical self-supervised task. We provide solid theoretical analyses to prove MetaMask can obtain tighter risk bounds for downstream classification compared to typical contrastive methods. Empirically, our method achieves state-of-the-art performance on various benchmarks.Comment: Accepted by NeurIPS 202

Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from such data. Accordingly, graph neural networks (GNNs) indeed achieve impressive success in various fields. Revisiting the GNN learning paradigms, we discover that the relationship between human expertise and the knowledge modeled by GNNs still confuses researchers. To this end, we introduce motivating experiments and derive an empirical observation that the human expertise is gradually learned by the GNNs in general domains. By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance. By exploring the intrinsic mechanism behind such observations, we elaborate the Structural Causal Model for the graph representation learning paradigm. Following the theoretical guidance, we innovatively introduce the auxiliary causal logic learning paradigm to improve the model to learn the expertise logic causally related to the graph representation learning task. In practice, the counterfactual technique is further performed to tackle the insufficient training issue during optimization. Plentiful experiments on the crafted and real-world domains support the consistent effectiveness of the proposed method

Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

While self-supervised learning techniques are often used to mining implicit knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a methodology, specifically consistency and complementarity network (CoCoNet), which avails of strict global inter-view consistency and local cross-view complementarity preserving regularization to comprehensively learn representations from multiple views. On the global stage, we reckon that the crucial knowledge is implicitly shared among views, and enhancing the encoder to capture such knowledge from data can improve the discriminability of the learned representations. Hence, preserving the global consistency of multiple views ensures the acquisition of common knowledge. CoCoNet aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric measurement based on the generalized sliced Wasserstein distance. Lastly on the local stage, we propose a heuristic complementarity-factor, which joints cross-view discriminative knowledge, and it guides the encoders to learn not only view-wise discriminability but also cross-view complementary information. Theoretically, we provide the information-theoretical-based analyses of our proposed CoCoNet. Empirically, to investigate the improvement gains of our approach, we conduct adequate experimental validations, which demonstrate that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin proves that such implicit consistency and complementarity preserving regularization can enhance the discriminability of latent representations.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE) 2022; Refer to https://ieeexplore.ieee.org/document/985763

A name alone is not enough: A reexamination of web-based personalization effect

Personalized information has become ubiquitous on the Internet. However, the conclusion on whether such information is always more effective than standardized information looks somewhat confusing in the literature. Some prior studies showed that a personalized message could generate more favorable outcomes than a standardized one, but others did not (sometimes with an almost identical study design). To provide a possible explanation why there existed such conflicting findings and conclusions in the personalization literature, the current study tested the influence of involvement on personalization in an advertising context. Through an experiment, it was found that the superiority of a personalized message over a standardized message was more salient when the message recipient was highly involved with the focal subject of the message than lowly involved. •How involvement might affect the personalization effect was tested in this study.•The effectiveness of personalization was examined in an online shopping context.•Personalized messages generated more favorable effects when involvement was high.•A simple personalization cue might not be sufficient to generate favorable effects

Effects of using social networking sites in different languages: Does Spanish or English make a difference?

Although a large volume of research on social networking sites (SNS) and their effects has been accumulated in the literature over the past few years, empirical studies examining how people use SNS in languages other than English are somewhat limited. Particularly, the use of Spanish SNS has rarely been investigated. To shed light on this research direction, the current study compared the effects of using Spanish and English SNS on individuals' cultural orientations and attitude formation. A total of 113 adult consumers participated in a laboratory experiment where they were asked to evaluate either of two experimental websites, one with Hispanic cultural connotations and the other with American cultural meanings. Their SNS usage and cultural orientations were measured. It was found that more frequent usage of Spanish SNS was significantly associated with a higher level of Hispanic cultural orientation and a more favorable attitude toward the website with American cultural meanings. However, the use of English SNS did not significantly influence people's American cultural orientations and their website evaluations. Usage of both Spanish and English SNS was found to be motivated by individuals' social play dependency on such media platforms. •People depend on social networking sites for the purpose of social play.•Using social networking sites in different languages can affect people's cultural orientations.•Using social networking sites in Spanish or English leads to different effects.•Social media usage may influence people's culture-related brand perceptions and evaluations

The Effect of Preference Stability and Extremity on Personalized Advertising

Personalized advertising is widely believed to be an effective persuasion strategy. A typical personalized advertising process consists of two phases: The message sender first “learns” the message receiver’s preferences, and then “matches” the message to that person according to his or her preferences. The present study argues that this process may be problematic because it assumes that an individual’s preferences are always stable (i.e., preferences remain the same over time) and extreme (i.e., preferences are highly polarized). Through a 2 (message type: personalized vs. nonpersonalized) × 2 (preference stability: high vs. low) × 2 (preference extremity: high vs. low) between-participants experiment ( N = 227), it is shown that the effectiveness of personalized advertising is moderated by preference stability and extremity. A new conceptualization of personalization is proposed based on the study results, and how the two phases of personalized advertising may be refined is highlighted. </jats:p

An analysis of how Fortune 500 companies respond to users replying to company tweets

With hundreds of millions of active users generating almost a half of a billion tweets each day, Twitter has solidified itself as one of the most popular websites in today’s digital world. Because of this popularity, companies seeking to leverage the large audience have gravitated toward Twitter. This study examines how the Fortune 500 uses Twitter by analyzing 9,122 corporate tweets and 1,509 user replies through the use of content analysis. Examined factors include interactivity (non-interactive vs. reactive vs. interactive), company type (B2B vs. B2C), and user reply valence (positive vs. neutral vs. negative). Company response time to user replies is also investigated. The study results point to interactive tweets generating the most engagement. B2Cs not only respond faster to user replies but also generate more engagement than B2Bs. Negative replies can decrease engagement for B2Bs and B2Cs, but the influence on B2Bs is more profound. Companies responded the fastest to negative replies followed by positive replies and neutral replies, respectively. Thus, a company should assess its own business practices, target audience, and ability to perform customer service before creating a social media account such as Twitter

Do Our Facebook Friends Make Us Feel Worse? A Study of Social Comparison and Emotion

People often compare themselves to others to gain a better understanding of the self in a process known as social comparison. The current study discusses how people engage in a social comparison process on Facebook, and how observing content from their Facebook friends may affect their emotions. A 2 (comparison direction) × 2 (relational closeness) × 2 (self‐esteem) between‐subjects experiment was conducted with 163 adult participants. The results revealed a significant 3‐way interaction such that people with high self‐esteem would be happier receiving positive information than negative information from their close friends, but the effect would be the opposite if the information was from a distant friend. There was no such difference for people with low self‐esteem