OpenRaiser/Intern-Atlas huggingface dataset:
Professional Network Matters: Connections Empower Person-Job Fit
Authors: Hao Chen, Lun Du, Yuxuan Lu, Qiang Fu, Xu Chen, Shi Han, Yanbin Kang, Guangming Lu, Zi Li
Venue: WSDM
Year: 2024

Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions. While existing works leverage historical or contextual information, they often disregard a crucial aspect: job seekers' social relationships in professional networks. This paper emphasizes the importance of incorporating professional networks into the Person-Job Fit model. Our innovative approach consists of two stages: (1) defining a Workplace Heterogeneous Information Network (WHIN) to capture heterogeneous knowledge, including professional connections and pre-training representations of various entities using a heterogeneous graph neural network; (2) designing a Contextual Social Attention Graph Neural Network (CSAGNN) that supplements users' missing information with professional connections' contextual information. We introduce a job-specific attention mechanism in CSAGNN to handle noisy professional networks, leveraging pre-trained entity representations from WHIN. We demonstrate the effectiveness of our approach through experimental evaluations conducted across three real-world recruitment datasets from LinkedIn, showing superior performance compared to baseline models.
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering
Authors: Huiyuan Chen, Vivian Lai, Hongye Jin, Zhimeng Jiang, Mahashweta Das, Xia Hu
Venue: WSDM
Year: 2024

Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to use contrastive loss to generate augmentation-invariant embeddings by maximizing the Mutual Information between different augmented views of the same instance. However, we empirically observe that existing CL models suffer from the dimensional collapse issue, where user/item embeddings only span a low-dimension subspace of the entire feature space. This suppresses other dimensional information and weakens the distinguishability of embeddings. Here we propose a non-contrastive learning objective, named nCL, which explicitly mitigates dimensional collapse of representations in collaborative filtering. Our nCL aims to achieve geometric properties of Alignment and Compactness on the embedding space. In particular, the alignment tries to push together representations of positive-related user-item pairs, while compactness tends to find the optimal coding length of user/item embeddings, subject to a given distortion. More importantly, our nCL does not require data augmentation nor negative sampling during training, making it scalable to large datasets compared to contrastive learning methods. Experimental results demonstrate the superiority of our nCL.
Empathetic Response Generation with Relation-aware Commonsense Knowledge
Authors: Changyu Chen, Yanran Li, Chen Wei, Jianwei Cui, Bin Wang, Rui Yan
Venue: WSDM
Year: 2024

The development of AI in mental health is a growing field with potential global impact. Machine agents need to perceive users' mental states and respond empathically. Since mental states are often latent and implicit, building such chatbots requires both knowledge learning and knowledge utilization. Our work contributes to this by developing a chatbot that aims to recognize and empathetically respond to users' mental states. We introduce a Conditional Variational Autoencoders (CVAE)-based model that utilizes relation-aware commonsense knowledge to generate responses. This model, while not a replacement for professional mental health support, demonstrates promise in offering informative and empathetic interactions in a controlled environment. On the dataset EmpatheticDialogues, we compare with several SOTA methods and empirically validate the effectiveness of our approach on response informativeness and empathy exhibition. Detailed analysis is also given to demonstrate the learning capability as well as model interpretability. Our code is accessible at http://github.com/ChangyuChen347/COMET-VAE.
Exploiting Duality in Open Information Extraction with Predicate Prompt
Authors: Zhen Chen, Jingping Liu, Deqing Yang, Yanghua Xiao, Huimin Xu, Zongyu Wang, Rui Xie, Yunsen Xian
Venue: WSDM
Year: 2024

Open information extraction (OpenIE) aims to extract the schema-free triplets in the form of (subject, predicate, object) from a given sentence. Compared with general information extraction (IE), OpenIE poses more challenges for the IE models, especially when multiple complicated triplets exist in a sentence. To extract these complicated triplets more effectively, in this paper we propose a novel generative OpenIE model, namely DualOIE, which achieves a dual task at the same time as extracting some triplets from the sentence, i.e., converting the triplets into the sentence. Such dual task encourages the model to correctly recognize the structure of the given sentence and thus is helpful to extract all potential triplets from the sentence. Specifically, DualOIE extracts the triplets in two steps: 1) first extracting a sequence of all potential predicates, 2) then using the predicate sequence as a prompt to induce the generation of triplets. Our experiments on two benchmarks and our dataset constructed from Meituan demonstrate that DualOIE achieves the best performance among the state-of-the-art baselines. Furthermore, the online A/B test on Meituan platform shows that 0.93% improvement of QV-CTR and 0.56% improvement of UV-CTR have been obtained when the triplets extracted by DualOIE were leveraged in Meituan's search system.
TemporalMed: Advancing Medical Dialogues with Time-Aware Responses in Large Language Models
Authors: Yuyan Chen, Jin Zhao, Zhihao Wen, Zhixu Li, Yanghua Xiao
Venue: WSDM
Year: 2024

Medical dialogue models predominantly emphasize generating coherent and clinically accurate responses. However, in many clinical scenarios, time plays a pivotal role, often dictating subsequent patient management and interventions. Recognizing the latent importance of temporal dynamics, this paper introduces a novel dimension to medical dialogues: timestamps. We advocate that the integration of time-sensitive directives can profoundly impact medical advice, using an illustrative example of post-surgery care with and without timestamps. Our contributions are three-fold: Firstly, we highlight the intrinsic significance of timestamps in medical conversations, marking a paradigm shift in dialogue modeling. Secondly, we present an innovative dataset and framework explicitly tailored for time-stamped medical dialogues, facilitating the model to not only provide medical counsel but also chronologically outline care regimens. Lastly, empirical evaluations indicate our method's proficiency in time-stamped tasks and reveal an uptick in performance in broader medical Q&A domains. Through our endeavors, we aspire to set new benchmarks in patient-centric and time-sensitive medical dialogue systems.
Customized and Robust Deep Neural Network Watermarking
Authors: Tzu-Yun Chien, Chih-Ya Shen
Venue: WSDM
Year: 2024

As the excellent performance of deep neural networks (DNNs) enhances a wide spectrum of applications, the protection of intellectual property (IP) of DNNs receives increasing attention recently, and DNN watermarking approaches are thus proposed for ownership verification to avoid potential misuses or thefts of DNN models. However, we observe that existing DNN watermark methods suffer from two major weaknesses: i) Incomplete protection to advanced watermark removal attacks, such as fine-tune attack with large learning rates, re-train after pruning, and most importantly, the distillation attack; ii) Limited customization ability, where multiple watermarked models cannot be uniquely identified, especially after removal attacks. To address these critical issues, we propose two new DNN watermarking approaches, Unified Soft-label Perturbation (USP), which provides robust watermark to detect model thefts, and Customized Soft-label Perturbation (CSP), which is able to embed a different watermark in each copy of the DNN model to enable customized watermarking. Experimental results show that our proposed USP and CSP resist all the watermark removal attacks, especially for the distillation attack, and the proposed CSP achieves very promising watermark customization ability, significantly outperforming the other state-of-the-art baselines.
What I Learned from Spending a Dozen Years in the Dark Web
Authors: Nicolas Christin
Venue: WSDM
Year: 2024

Founded in 2011, Silk Road was the first online anonymous marketplace, in which buyers and sellers could transact with anonymity guarantees far superior to those available in online or offline alternatives, thanks to the innovative use of cryptocurrencies and network anonymization. Business on Silk Road, primarily involving narcotics trafficking, was brisk and before long competitors appeared. After Silk Road was taken down by law enforcement, a dynamic ecosystem of online anonymous marketplaces emerged. That ecosystem is highly active, to this day, and has been surprisingly resilient to multiple law enforcement take down operations as well as ''exit scams,'' in which the operators of a marketplace abruptly abscond with any money left on the platform. In this talk, I describe insights we have gained from more than twelve years of active measurements of the online anonymous market ecosystem [1,2,3,4,5,6]. I first highlight the scientific challenges in collecting such data at scale, discuss how overall revenues rapidly grew to hundreds of millions of dollars per year, and describe the leading types of commerce taking place - primarily narcotics, but also cybercrime commoditization. Second, I present several analyses of vendors, ranging from our efforts to match a priori disparate handles to unique individuals, to predicting whether a vendor will be successful in the future. Finally, I introduce some of the unique data we could access, namely backend data from police seizures, and show how we used it to validate our measurements. While online anonymous marketplaces have recently seen their influence dip a bit - possibly due to a combination of constant infighting and relentless police activity - the insights we gained from studying them for so long help considerably demystify the "dark web," and more generally, online crime ecosystems, which turn out to be very economically rational environments.
Automated Topic Generation for the Mexican Platform for Access to Government Public Information During the Period 2003-2020
Authors: Hermelando Cruz-Pérez, Alejandro Molina-Villegas
Venue: WSDM
Year: 2024

The right of access to public information, as safeguarded by national laws and international agreements, is a fundamental guarantee that ensures the ability of every citizen to request and receive government-generated information. In Mexico, the National Institute for Transparency, Access to Information, and Protection of Personal Data (INAI) is responsible for upholding this right. INAI has implemented policies aimed at enhancing access to information, with the National Transparency Platform (NPT) being a notable initiative \citeford2016evolucion . On this platform, individuals from around the world can submit information requests to the Mexican government.
Applications of LLMs in E-Commerce Search and Product Knowledge Graph: The DoorDash Case Study
Authors: Sudeep Das, Raghav Saboo, Chaitanya S. K. Vadrevu, Bruce Wang, Steven Xu
Venue: WSDM
Year: 2024

Extracting knowledge from unstructured or semi-structured textual information is essential for the machine learning applications that power DoorDash's search experience, and the development and maintenance of its product knowledge graph. Large language models (LLMs) have opened up new possibilities for utilizing their power in these areas, replacing or complementing traditional natural language processing methods. LLMs are also proving to be useful in the label and annotation generation process, which is critical for these use cases. In this talk, we will provide a high-level overview of how we incorporated LLMs for search relevance and product understanding use cases, as well as the key lessons learned and challenges faced during their practical implementation.
The 3rd International Workshop on Interactive and Scalable Information Retrieval Methods for eCommerce (ISIR-eCom 2024)
Authors: Vachik S. Dave, Linsey Pang, Xiquan Cui, Chen Luo, Hamed Zamani, Lingfei Wu, George Karypis
Venue: WSDM
Year: 2024

Over the past few years, consumer behavior has shifted from traditional in-store shopping to online shopping. For example, eCommerce sales have grown from around 5% of total US sales in 2012 to around 15.4% in year 2023. This rapid growth of eCommerce has created new challenges and vital new requirements for intelligent information retrieval systems. Which lead to the primary motivations of this workshop:
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Authors: Shohreh Deldari, Dimitris Spathis, Mohammad Malekzadeh, Fahim Kawsar, Flora D. Salim, Akhil Mathur
Venue: WSDM
Year: 2024

Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field. Self-supervised learning (SSL) is a promising approach to learn data representations without relying on labels. However, existing SSL methods require expensive computations of negative pairs and are typically designed for single modalities, which limits their versatility. We introduce CroSSL (Cross-modal SSL), which puts forward two novel concepts: masking intermediate embeddings produced by modality-specific encoders, and their aggregation into a global embedding through a cross-modal aggregator CroSSL allows for handling missing modalities and end-to-end cross-modal earning without requiring prior data preprocessing for handling missing inputs or negative-pair sampling for contrastive learning. We evaluate our method on a wide range of data, including motion sensors such as accelerometers or gyroscopes and biosignals (heart rate, electroencephalograms, electromyograms, electrooculograms, and electrodermal). Overall, CroSSL outperforms previous SSL and supervised benchmarks using minimal labeled data, and also sheds light on how latent masking can improve cross-modal learning.
CL4DIV: A Contrastive Learning Framework for Search Result Diversification
Authors: Zhirui Deng, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen
Venue: WSDM
Year: 2024

Search result diversification aims to provide a diversified document ranking list so as to cover as many intents as possible and satisfy the various information needs of different users. Existing approaches usually represented documents by pretrained embeddings (such as doc2vec and Glove). These document representations cannot adequately represent the document's content and are hard to capture the intrinsic user's intent coverage of the given query. Moreover, the limited number of labeled data for search result diversification exacerbates the difficulty of obtaining more efficient document representations. To alleviate these problems and learn more effective document representations, we propose a Contrastive Learning framework for search result DIVersification (CL4DIV). Specifically, we design three contrastive learning tasks from the perspective of subtopics, documents, and candidate document sequences, which correspond to three essential elements in search result diversification. These training tasks are employed to pretrain the document encoder and the document sequence encoder, which are used in the diversified ranking model. Experimental results show that øurs significantly outperforms all existing diversification models. Further analysis demonstrates that our method has wide applicability and can also be used to improve several existing methods.
The 5th International Workshop on Machine Learning on Graphs (MLoG)
Authors: Tyler Derr, Yao Ma, Kaize Ding, Tong Zhao, Nesreen K. Ahmed
Venue: WSDM
Year: 2024

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 5th International Workshop on Machine Learning on Graphs (MLoG) (https://mlog-workshop.github.io/wsdm24.html), held in conjunction with the 17th 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.
The Journey to A Knowledgeable Assistant with Retrieval-Augmented Generation (RAG)
Authors: Xin Luna Dong
Venue: WSDM
Year: 2024

Large Language Models (LLMs) have demonstrated strong capabilities in comprehending and generating human language, as well as emerging abilities like reasoning and using tools. These advancements have been revolutionizing techniques in every front, including the development of personal assistants. However, their inherent limitations such as lack of factuality and hallucinations make LLMs less suitable for creating knowledgeable and trustworthy assistants. In this talk, we describe our journey in building a knowledgeable AI assistant by harnessing LLM techniques. We start with a comprehensive set of experiments designed to answer the questions of \em how reliable are LLMs on answering factual questions and \em how the performance differs across different types of factual knowledge. Subsequently, we constructed a \em federated Retrieval-Augmented Generation (RAG) system that integrates external information from both the web and knowledge graphs in text generation. This system supports conversation functionality for the Ray-ban Meta smart glasses, providing trustworthy information on real-time topics like stocks and sports, and information on torso-to-tail entities such as local restaurants. Additionally, we are exploring the potential of external knowledge to facilitate multi-modal Q&A. We will share our techniques, our findings, and the path forward in this talk.
Genomic-World Fungi Data: Synteny Part
Authors: Pedro Escobar-Turriza, Luis Muñoz-Miranda, Alejandro Pereira-Santana
Venue: WSDM
Year: 2024

Fungal genomes undergo duplication events such as Whole Genome Duplication (WGD), which contribute to rapid adaptation and increased genetic diversity. The results provide insights into genome integrity, completeness, evolutionary patterns, and functional conservation observed across different fungal species. Highly conserved synteny patterns underscore the significance of cell membrane proteins, proteolytic capabilities, dynein motor proteins, and endocytosis processes in Basidiomycota, supporting the essential roles these processes play in their development throughout evolution. This research establishes the groundwork for further exploration of genomic diversity and potential applications of synteny analysis in elucidating evolutionary patterns and ecological relationships among fungal species.
Scaling Use-case Based Shopping using LLMs
Authors: Sachin Farfade, Sachin Vernekar, Vineet Chaoji, Rajdeep Mukherjee
Venue: WSDM
Year: 2024

Products on e-commerce websites are usually organized based on seller-provided product attributes. Customers looking for a product typically have certain needs or product use-cases in mind, for e.g., a headphone for gym classes, or a printer for a small business. However, they often struggle to map these use-cases to product attributes and subsequently fail to find the product they need. In this talk, we present a use-case based shopping (UBS) ML system that facilitates use-case based customer experiences (CXs). The UBS system recommends dominant product use-cases to customers along with most relevant products for those use-cases. Use-cases and their definitions vary across product categories and marketplaces (MPs). This makes training supervised models for thousands of e-commerce categories and multiple MPs infeasible by collecting large amount training data needed to train these models. In this talk, we present our work on scaling the UBS model by instruction tuning an LLM for our task.
Contextual MAB Oriented Embedding Denoising for Sequential Recommendation
Authors: Zhichao Feng, Pengfei Wang, Kaiyuan Li, Chenliang Li, Shangguang Wang
Venue: WSDM
Year: 2024

Deep neural networks now have become the de-facto standard for sequential recommendation. In the existing techniques, an embedding vector is assigned for each item, encoding all the characteristics of the latter in latent space. Then, the recommendation is transferred to devising a similarity metric to recommend user's next behavior. Here, we consider each dimension of an embedding vector as a (latent) feature. Though effective, it is unknown which feature carries what semantics toward the item. Actually, in reality, this merit is highly preferable since a specific group of features could induce a particular relation among the items while the others are in vain. Unfortunately, the previous treatment overlooks the feature semantic learning at such a fine-grained level. When each item contains multiple latent aspects, which however is prevalent in real-world, the relations between items are very complex. The existing solutions are easy to fail on better recommendation performance. It is necessary to disentangle the item embeddings and extract credible features in a context-aware manner. To address this issue, in this work, we present a novel Co ntextual M AB based E mbedding D enoising model~(COMED for short) to adaptively identify relevant dimension-level features for a better recommendation. Specifically, COMED formulates the embedding denoising task as a Contextual Multi-armed Bandit problem. For each feature of the item embedding, we assign a two-armed neural bandit to determine whether the constituent semantics should be preserved, rendering the whole process as embedding denoising. By aggregating the denoised embeddings as contextual information, a reward function deduced by the similarity between the historical interaction sequence and the target item is further designed to approximate the maximum expected payoffs of bandits for efficient learning. Considering the possible inefficiency of training the serial operating mechanism, we also design a swift learning strategy to accelerate the co-guidance between the renovated sequential embedding and the parallel actions of neural bandits for a better recommendation. Comprehensive trials conducted on four widely recognized benchmarks substantiate the efficiency and efficacy of our framework.