OpenRaiser/Intern-Atlas huggingface dataset:
Relation Preference Oriented High-order Sampling for Recommendation
Authors: Mukun Chen, Xiuwen Gong, YH Jin, Wenbin Hu
Venue: WSDM
Year: 2023

The introduction of knowledge graphs (KG) into recommendation systems (RS) has been proven to be effective because KG introduces a variety of relations between items. In fact, users have different relation preferences depending on the relationship in KG. Existing GNN-based models largely adopt random neighbor sampling strategies to process propagation; however, these models cannot aggregate biased relation preference local information for a specific user, and thus cannot effectively reveal the internal relationship between users' preferences. This will reduce the accuracy of recommendations, while also limiting the interpretability of the results. Therefore, we propose a Relation Preference oriented High-order Sampling (RPHS) model to dynamically sample subgraphs based on relation preference and hard negative samples for user-item pairs. We design a path sampling strategy based on relation preference, which can encode the critical paths between specific user-item pairs to sample the paths in the high-order message passing subgraphs. Next, we design a mixed sampling strategy and define a new propagation operation to further enhance RPHS's ability to distinguish negative signals. Through the above sampling strategies, our model can better aggregate local relation preference information and reveal the internal relationship between users' preferences. Experiments show that our model outperforms the state-of-the-art models on three datasets by 14.98%, 5.31%, and 8.65%, and also performs well in terms of interpretability. The codes are available at https://github.com/RPHS/RPHS.git
Heterogeneous Graph Contrastive Learning for Recommendation
Authors: Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, Ronghua Luo
Venue: WSDM
Year: 2023

Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured data in recommender systems. However, real-life recommendation scenarios usually involve heterogeneous relationships (e.g., social-aware user influence, knowledge-aware item dependency) which contains fruitful information to enhance the user preference learning. In this paper, we study the problem of heterogeneous graph-enhanced relational learning for recommendation. Recently, contrastive self-supervised learning has become successful in recommendation. In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views. However, the influence of heterogeneous side information on interactions may vary by users and items. To move this idea forward, we enhance our heterogeneous graph contrastive learning with meta networks to allow the personalized knowledge transformer with adaptive contrastive augmentation. The experimental results on three real-world datasets demonstrate the superiority of HGCL over state-of-the-art recommendation methods. Through ablation study, key components in HGCL method are validated to benefit the recommendation performance improvement. The source code of the model implementation is available at the link https://github.com/HKUDS/HGCL.
Unsupervised Question Duplicate and Related Questions Detection in e-learning platforms
Authors: Maksimjeet Chowdhary, Sanyam Goyal, Venktesh V, Mukesh K. Mohania, Vikram Goyal
Venue: WSDM
Year: 2023

Online learning platforms provide diverse questions to gauge the learners' understanding of different concepts. The repository of questions has to be constantly updated to ensure a diverse pool of questions to conduct assessments for learners. However, it is impossible for the academician to manually skim through the large repository of questions to check for duplicates when onboarding new questions from external sources. Hence, we propose a toolQDup in this paper that can surface near-duplicate and semantically related questions without any supervised data. The proposed tool follows an unsupervised hybrid pipeline of statistical and neural approaches for incorporating different nuances in similarity for the task of question duplicate detection. We demonstrate thatQDup can detect near-duplicate questions and also suggest related questions for practice with remarkable accuracy and speed from a large repository of questions. The demo video of the tool can be found at https://www.youtube.com/watch?v=loh0_-7XLW4.
Meta Policy Learning for Cold-Start Conversational Recommendation
Authors: Zhendong Chu, Hongning Wang, Yun Xiao, Bo Long, Lingfei Wu
Venue: WSDM
Year: 2023

Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained by reinforcement learning for a population of users. However, for users new to the system, such a global policy becomes ineffective to satisfy them, i.e., the cold-start challenge. In this paper, we study CRS policy learning for cold-start users via meta reinforcement learning. We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations. To facilitate fast policy adaptation, we design three synergetic components. Firstly, we design a meta-exploration policy dedicated to identifying user preferences via a few exploratory conversations, which accelerates personalized policy adaptation from the meta policy. Secondly, we adapt the item recommendation module for each user to maximize the recommendation quality based on the collected conversation states during conversations. Thirdly, we propose a Transformer-based state encoder as the backbone to connect the previous two components. It provides comprehensive state representations by modeling complicated relations between positive and negative feedback during the conversation. Extensive experiments on three datasets demonstrate the advantage of our solution in serving new users, compared with a rich set of state-of-the-art CRS solutions.
Classification of Different Participating Entities in the Rise of Hateful Content in Social Media
Authors: Mithun Das
Venue: WSDM
Year: 2023

Hateful content is a growing concern across different platforms, whether it is a moderated platform or an unmoderated platform. The public expression of hate speech encourages the devaluation of minority members. It has some consequences in the real world as well. In such a scenario, it is necessary to design AI systems that could detect such harmful entities/elements in online social media and take cautionary actions to mitigate the risk/harm they cause to society. The way individuals disseminate content on social media platforms also deviates. The content can be in the form of texts, images, videos, etc. Hence hateful content in all forms should be detected, and further actions should be taken to maintain the civility of the platform. We first introduced two published works addressing the challenges of detecting low-resource multilingual abusive speech and hateful user detection. Finally, we discuss our ongoing work on multimodal hateful content detection.
Generative Slate Recommendation with Reinforcement Learning
Authors: Romain Deffayet, Thibaut Thonet, Jean-Michel Renders, Maarten de Rijke
Venue: WSDM
Year: 2023

Recent research has employed reinforcement learning (RL) algorithms to optimize long-term user engagement in recommender systems, thereby avoiding common pitfalls such as user boredom and filter bubbles. They capture the sequential and interactive nature of recommendations, and thus offer a principled way to deal with long-term rewards and avoid myopic behaviors. However, RL approaches are intractable in the slate recommendation scenario - where a list of items is recommended at each interaction turn - due to the combinatorial action space. In that setting, an action corresponds to a slate that may contain any combination of items. While previous work has proposed well-chosen decompositions of actions so as to ensure tractability, these rely on restrictive and sometimes unrealistic assumptions. Instead, in this work we propose to encode slates in a continuous, low-dimensional latent space learned by a variational auto-encoder. Then, the RL agent selects continuous actions in this latent space, which are ultimately decoded into the corresponding slates. By doing so, we are able to (i) relax assumptions required by previous work, and (ii) improve the quality of the action selection by modeling full slates instead of independent items, in particular by enabling diversity. Our experiments performed on a wide array of simulated environments confirm the effectiveness of our generative modeling of slates over baselines in practical scenarios where the restrictive assumptions underlying the baselines are lifted. Our findings suggest that representation learning using generative models is a promising direction towards generalizable RL-based slate recommendation.
Zero to Hero: Exploiting Null Effects to Achieve Variance Reduction in Experiments with One-sided Triggering
Authors: Alex Deng, Lo-Hua Yuan, Naoya Kanai, Alexandre Salama-Manteau
Venue: WSDM
Year: 2023

In online experiments where the intervention is only exposed, or "triggered", for a small subset of the population, it is critical to use variance reduction techniques to estimate treatment effects with sufficient precision to inform business decisions. Trigger-dilute analysis is often used in these situations, and reduce the sampling variance of overall intent-to-treat (ITT) effects by an order of magnitude equal to the inverse of the triggering rate; for example, a triggering rate of 5% corresponds to roughly a 20x reduction in variance. To apply trigger-dilute analysis, one needs to know experimental subjects' triggering counterfactual statuses, i.e., the counterfactual behavior of subjects under both treatment and control conditions. In this paper, we propose an unbiased ITT estimator with reduced variance applicable for experiments where the triggering counterfactual status is only observed in the treatment group. Our method is based on the efficiency augmentation idea of CUPED and draws upon identification frameworks from the principal stratification and instrumental variables literature. The unbiasedness of our estimation approach relies on a testable assumption that the augmentation term used for covariate adjustment equals zero in expectation. Unlike traditional covariate adjustment or principal score modeling approaches, our estimator can incorporate both pre-experiment and in-experiment observations. We demonstrate through both a real-world experiment and simulations that our estimator can remain unbiased and achieve precision improvements as large as if triggering status were fully observed, and in some cases can even outperform trigger-dilute analysis.
The 3rd International Workshop on Machine Learning on Graphs (MLoG)
Authors: Tyler Derr, Yao Ma, Benedek Rozemberczki, Neil Shah, Shirui Pan
Venue: WSDM
Year: 2023

Graphs, which encode pairwise relations between entities, are a kind of universal data structure for a lot of real-world data, including social networks, transportation networks, and chemical molecules. Many important applications on these data can be treated as computational tasks on graphs. Recently, machine learning techniques are widely developed and utilized to effectively tame graphs for discovering actionable patterns and harnessing them for advancing various graph-related computational tasks. Huge success has been achieved and numerous real-world applications have benefited from it. However, since in today's world, we are generating and gathering data in a much faster and more diverse way, real-world graphs are becoming increasingly large-scale and complex. More dedicated efforts are needed to propose more advanced machine learning techniques and properly deploy them for real-world applications in a scalable way. Thus, we organize The 3rd International Workshop on Machine Learning on Graphs (MLoG), held in conjunction with the 16th ACM Conference on Web Search and Data Mining (WSDM), which provides a venue to gather academia researchers and industry researchers/practitioners to present the recent progress on machine learning on graphs.
DistriBayes: A Distributed Platform for Learning, Inference and Attribution on Large Scale Bayesian Network
Authors: Yi Ding, Jun Zhou, Qing Cui, Lin Wang, Mengqi Zhang, Yang Dong
Venue: WSDM
Year: 2023

To improve the marketing performance in the financial scenario, it is necessary to develop a trustworthy model to analyze and select promotion-sensitive customers. Bayesian Network (BN) is suitable for this task because of its interpretability and flexibility, but it usually suffers the exponentially growing computation complexity as the number of nodes grows. To tackle this problem, we present a comprehensive distributed platform named DistriBayes, which can efficiently learn, infer and attribute on a large-scale BN all-in-one platform. It implements several score-based structure learning methods, loopy belief propagation with backdoor adjustment for inference, and a carefully optimized search procedure for attribution. Leveraging the distributed cluster, DistriBayes can finish the learning and attribution on Bayesian Network with hundreds of nodes and millions of samples in hours.
Active Ensemble Learning for Knowledge Graph Error Detection
Authors: Junnan Dong, Qinggang Zhang, Xiao Huang, Qiaoyu Tan, Daochen Zha, Zihao Zhao
Venue: WSDM
Year: 2023

Knowledge graphs (KGs) could effectively integrate a large number of real-world assertions, and improve the performance of various applications, such as recommendation and search. KG error detection has been intensively studied since real-world KGs inevitably contain erroneous triples. While existing studies focus on developing a novel algorithm dedicated to one or a few data characteristics, we explore advancing KG error detection by assembling a set of state-of-the-art (SOTA) KG error detectors. However, it is nontrivial to develop a practical ensemble learning framework for KG error detection. Existing ensemble learning models heavily rely on labels, while it is expensive to acquire labeled errors in KGs. Also, KG error detection itself is challenging since triples contain rich semantic information and might be false because of various reasons. To this end, we propose to leverage active learning to minimize human efforts. Our proposed framework - KAEL, could effectively assemble a set of off-the-shelf error detection algorithms, by actively using a limited number of manual annotations. It adaptively updates the ensemble learning policy in each iteration based on active queries, i.e., the answers from experts. After all annotation budget is used, KAEL utilizes the trained policy to identify remaining suspicious triples. Experiments on real-world KGs demonstrate that we can achieve significant improvement when applying KAEL to assemble SOTA error detectors. KAEL also outperforms SOTA ensemble learning baselines significantly.
Multi-queue Momentum Contrast for Microvideo-Product Retrieval
Authors: Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie
Venue: WSDM
Year: 2023

The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their micro-videos, which not only provides them with business income but helps the audiences to discover their interesting products. However, due to the micro-video recording by unprofessional equipment, involving various topics and including multiple modalities, it is challenging to locate the products related to micro-videos efficiently, appropriately, and accurately. We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances. A novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval, consisting of the uni-modal feature and multi-modal instance representation learning. Moreover, a discriminative selection strategy with a multi-queue is used to distinguish the importance of different negatives based on their categories. We collect two large-scale microvideo-product datasets (MVS and MVS-large) for evaluation and manually construct the hierarchical category ontology, which covers sundry products in daily life. Extensive experiments show that MQMC outperforms the state-of-the-art baselines. Our replication package (including code, dataset, etc.) is publicly available at https://github.com/duyali2000/MQMC.
IDNP: Interest Dynamics Modeling Using Generative Neural Processes for Sequential Recommendation
Authors: Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao
Venue: WSDM
Year: 2023

Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) short-term: interaction sequences may not result from a monolithic interest, but rather from several intertwined interests, even within a short period of time, resulting in their failures to model skip behaviors; (2) long-term: interaction sequences are primarily observed sparsely at discrete intervals, other than consecutively over the long run. This renders difficulty in inferring long-term interests, since only discrete interest representations can be derived, without taking into account interest dynamics across sequences. In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests. To this end, we present an Interest Dynamics modeling framework using generative Neural Processes, coined IDNP, to model user interests from a functional perspective. IDNP learns a global interest function family to define each user's long-term interest as a function instantiation, manifesting interest dynamics through function continuity. Specifically, IDNP first encodes each user's short-term interactions into multi-scale representations, which are then summarized as user context. By combining latent global interest with user context, IDNP then reconstructs long-term user interest functions and predicts interactions at upcoming query timestep. Moreover, IDNP can model such interest functions even when interaction sequences are limited and non-consecutive. Extensive experiments on four real-world datasets demonstrate that our model outperforms the state-of-the-art on various evaluation metrics.
Pairwise Fairness in Ranking as a Dissatisfaction Measure
Authors: Alessandro Fabris, Gianmaria Silvello, Gian Antonio Susto, Asia J. Biega
Venue: WSDM
Year: 2023

Fairness and equity have become central to ranking problems in information access systems, such as search engines, recommender systems, or marketplaces. To date, several types of fair ranking measures have been proposed, including diversity, exposure, and pairwise fairness measures. Out of those, pairwise fairness is a family of metrics whose normative grounding has not been clearly explicated, leading to uncertainty with respect to the construct that is being measured and how it relates to stakeholders' desiderata. In this paper, we develop a normative and behavioral grounding for pairwise fairness in ranking. Leveraging measurement theory and user browsing models, we derive an interpretation of pairwise fairness centered on the construct of producer dissatisfaction, tying pairwise fairness to perceptions of ranking quality. Highlighting the key limitations of prior pairwise measures, we introduce a set of reformulations that allow us to capture behavioral and practical aspects of ranking systems. These reformulations form the basis for a novel pairwise metric of producer dissatisfaction. Our analytical and empirical study demonstrates the relationship between dissatisfaction, pairwise, and exposure-based fairness metrics, enabling informed adoption of the measures.
Metropolitan-scale Mobility Digital Twin
Authors: Zipei Fan, Renhe Jiang, Ryosuke Shibasaki
Venue: WSDM
Year: 2023

Mobility digital twin, which is a a virtual replica of the mobility in the physical world, is the key building block of modern smart city applications at a metropolitan scale, including traffic regulation, emergency management and epidemic control. To duplicate the mobility in the physical world and show the potential outcome based on either the current state or manipulated conditions, we are facing with three main challenges: 1) how to sense real-time human mobility at a large scale and assimilate different data sources to infer a dynamic city mobility state; 2) how to make an accurate prediction for the mobility replica that adapts with dynamic city mobility state; 3) how to simulate the mobility in response to different conditions. In this talk, we will present our recent studies, practices and perspectives on mobility digital twin in addressing these above challenges, with applications in the real-world scenarios with industrial connections.
Cooperative Explanations of Graph Neural Networks
Authors: Junfeng Fang, Xiang Wang, An Zhang, Zemin Liu, Xiangnan He, Tat-Seng Chua
Venue: WSDM
Year: 2023

With the growing success of graph neural networks (GNNs), the explainability of GNN is attracting considerable attention. Current explainers mostly leverage feature attribution and selection to explain a prediction. By tracing the importance of input features, they select the salient subgraph as the explanation. However, their explainability is at the granularity of input features only, and cannot reveal the usefulness of hidden neurons. This inherent limitation makes the explainers fail to scrutinize the model behavior thoroughly, resulting in unfaithful explanations. In this work, we explore the explainability of GNNs at the granularity of both input features and hidden neurons. To this end, we propose an explainer-agnostic framework, Cooperative GNN Explanation (CGE) to generate the explanatory subgraph and subnetwork simultaneously, which jointly explain how the GNN model arrived at its prediction. Specifically, it first initializes the importance scores of input features and hidden neurons with masking networks. Then it iteratively retrains the importance scores, refining the salient subgraph and subnetwork by discarding low-scored features and neurons in each iteration. Through such cooperative learning, CGE not only generates faithful and concise explanations, but also exhibits how the salient information flows by activating and deactivating neurons. We conduct extensive experiments on both synthetic and real-world datasets, validating the superiority of CGE over state-of-the-art approaches. Code is available at https://github.com/MangoKiller/CGE_demo.