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- research-article
Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation
ACM Transactions on Knowledge Discovery from Data, Volume 19, Issue 1•January 2025, Article No.: 23, pp 1-26 • https://doi.org/10.1145/3701563Distributed graph neural network (GNN) training facilitates learning on massive graphs that surpass the storage and computational capabilities of a single machine. Traditional distributed frameworks strive for performance parity with centralized training ...
- research-article
Probabilistic entity representation model for reasoning over knowledge graphs
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems•December 2021, Article No.: 1795, pp 23440-23451Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique that can provide efficient querying mechanism over large and incomplete databases. Current approaches employ spatial geometries such as boxes to learn query representations that ...
- research-article
Hyperbolic graph neural networks at scale: a meta learning approach
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems•December 2023, Article No.: 1926, pp 44488-44501The progress in hyperbolic neural networks (HNNs) research is hindered by their absence of inductive bias mechanisms, which are essential for generalizing to new tasks and facilitating scalable learning over large datasets. In this paper, we aim to ...
- research-article
MS MARCO Web Search: A Large-scale Information-rich Web Dataset with Millions of Real Click Labels
- Qi Chen,
- Xiubo Geng,
- Corby Rosset,
- Carolyn Buractaon,
- Jingwen Lu,
- Tao Shen,
- Kun Zhou,
- Chenyan Xiong,
- Yeyun Gong,
- Paul Bennett,
- Nick Craswell,
- Xing Xie,
- Fan Yang,
- Bryan Tower,
- Nikhil Rao,
- Anlei Dong,
- Wenqi Jiang,
- Zheng Liu,
- Mingqin Li,
- Chuanjie Liu,
- Zengzhong Li,
- Rangan Majumder,
- Jennifer Neville,
- Andy Oakley,
- Knut Magne Risvik,
- Harsha Vardhan Simhadri,
- Manik Varma,
- Yujing Wang,
- Linjun Yang,
- Mao Yang,
- Ce Zhang
WWW '24: Companion Proceedings of the ACM Web Conference 2024•May 2024, pp 292-301• https://doi.org/10.1145/3589335.3648327Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked ...
- research-article
Task-agnostic graph explanations
NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing Systems•November 2022, Article No.: 874, pp 12027-12039Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph-structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph-structured data. Existing learning-...
- research-article
Maximizing and satisficing in multi-armed bandits with graph information
NIPS '22: Proceedings of the 36th International Conference on Neural Information Processing Systems•November 2022, Article No.: 147, pp 2019-2032Pure exploration in multi-armed bandits has emerged as an important framework for modeling decision making and search under uncertainty. In modern applications however, one is often faced with a tremendously large number of options and even obtaining one ...
- research-article
Search Behavior Prediction: A Hypergraph Perspective
WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining•February 2023, pp 697-705• https://doi.org/10.1145/3539597.3570403At E-Commerce stores such as Amazon, eBay, and Taobao, the shopping items and the query words that customers use to search for the items form a bipartite graph that captures search behavior. Such a query-item graph can be used to forecast search trends ...
- tutorial
Accepted Tutorials at The Web Conference 2022
- Riccardo Tommasini,
- Senjuti Basu Roy,
- Xuan Wang,
- Hongwei Wang,
- Heng Ji,
- Jiawei Han,
- Preslav Nakov,
- Giovanni Da San Martino,
- Firoj Alam,
- Markus Schedl,
- Elisabeth Lex,
- Akash Bharadwaj,
- Graham Cormode,
- Milan Dojchinovski,
- Jan Forberg,
- Johannes Frey,
- Pieter Bonte,
- Marco Balduini,
- Matteo Belcao,
- Emanuele Della Valle,
- Junliang Yu,
- Hongzhi Yin,
- Tong Chen,
- Haochen Liu,
- Yiqi Wang,
- Wenqi Fan,
- Xiaorui Liu,
- Jamell Dacon,
- Lingjuan Lye,
- Jiliang Tang,
- Aristides Gionis,
- Stefan Neumann,
- Bruno Ordozgoiti,
- Simon Razniewski,
- Hiba Arnaout,
- Shrestha Ghosh,
- Fabian Suchanek,
- Lingfei Wu,
- Yu Chen,
- Yunyao Li,
- Bang Liu,
- Filip Ilievski,
- Daniel Garijo,
- Hans Chalupsky,
- Pedro Szekely,
- Ilias Kanellos,
- Dimitris Sacharidis,
- Thanasis Vergoulis,
- Nurendra Choudhary,
- Nikhil Rao,
- Karthik Subbian,
- Srinivasan Sengamedu,
- Chandan K. Reddy,
- Friedhelm Victor,
- Bernhard Haslhofer,
- George Katsogiannis- Meimarakis,
- Georgia Koutrika,
- Shengmin Jin,
- Danai Koutra,
- Reza Zafarani,
- Yulia Tsvetkov,
- Vidhisha Balachandran,
- Sachin Kumar,
- Xiangyu Zhao,
- Bo Chen,
- Huifeng Guo,
- Yejing Wang,
- Ruiming Tang,
- Yang Zhang,
- Wenjie Wang,
- Peng Wu,
- Fuli Feng,
- Xiangnan He
WWW '22: Companion Proceedings of the Web Conference 2022•April 2022, pp 391-399• https://doi.org/10.1145/3487553.3547182This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on.
- abstract
Hyperbolic Neural Networks: Theory, Architectures and Applications
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 4778-4779• https://doi.org/10.1145/3534678.3542613Recent studies have revealed important properties that are unique to graph datasets such as hierarchies and global structures. This has driven research into hyperbolic space due to their ability to effectively encode the inherent hierarchy present in ...
- research-article
Learning Backward Compatible Embeddings
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 3018-3028• https://doi.org/10.1145/3534678.3539194Embeddings, low-dimensional vector representation of objects, are fundamental in building modern machine learning systems. In industrial settings, there is usually an embedding team that trains an embedding model to solve intended tasks (e.g., product ...
- research-article
Graph-based Multilingual Language Model: Leveraging Product Relations for Search Relevance
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 2789-2799• https://doi.org/10.1145/3534678.3539158The large-scale nature of product catalog and the changing demands of customer queries makes product search a challenging problem. The customer queries are ambiguous and implicit. They may be looking for an exact match of their query, or a functional ...
- research-article
ALLIE: Active Learning on Large-scale Imbalanced Graphs
WWW '22: Proceedings of the ACM Web Conference 2022•April 2022, pp 690-698• https://doi.org/10.1145/3485447.3512229Human labeling is time-consuming and costly. This problem is further exacerbated in extremely imbalanced class label scenarios, such as detecting fraudsters in online websites. Active learning selects the most relevant example for human labelers to ...
- research-article
ANTHEM: Attentive Hyperbolic Entity Model for Product Search
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining•February 2022, pp 161-171• https://doi.org/10.1145/3488560.3498456Product search is a fundamentally challenging problem due to the large-size of product catalogues and the complexity of extracting semantic information from products. In addition to this, the black-box nature of most search systems also hamper a smooth ...
- abstract
Workshop on Data-Efficient Machine Learning (DeMaL)
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining•August 2021, pp 4135-4136• https://doi.org/10.1145/3447548.3469451The recent increase in the size of neural networks has led to a proportional increase in the demands for high-quality human-annotated data. Labeling data is a costly and time-consuming endeavor, and the need for large data is often satiated through ...
- research-article
Bipartite Dynamic Representations for Abuse Detection
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining•August 2021, pp 3638-3648• https://doi.org/10.1145/3447548.3467141Abusive behavior in online retail websites and communities threatens the experience of regular community members. Such behavior often takes place within a complex, dynamic, and large-scale network of users interacting with items. Detecting abuse is ...
- abstract
Learning with Little Data: Industry Challenges and Innovations
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2021, pp 2625-2626• https://doi.org/10.1145/3404835.3464925In e-commerce applications, customers search and discover one or more products using queries. Some of these queries are broad and diverse, with multiple intents. Therefore, relying purely on the anonymized and aggregated customer historical behavioral ...
- research-article
Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs
WWW '21: Proceedings of the Web Conference 2021•April 2021, pp 1373-1384• https://doi.org/10.1145/3442381.3449974Knowledge Graphs (KGs) are ubiquitous structures for information storage in several real-world applications such as web search, e-commerce, social networks, and biology. Querying KGs remains a foundational and challenging problem due to their size and ...
- research-article
Language-Agnostic Representation Learning for Product Search on E-Commerce Platforms
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining•January 2020, pp 7-15• https://doi.org/10.1145/3336191.3371852Product search forms an indispensable component of any e-commerce service, and helps customers find products of their interest from a large catalog on these websites. When products that are irrelevant to the search query are surfaced, it leads to a poor ...
- research-article
Identifying Facet Mismatches In Search Via Micrographs
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management•November 2019, pp 1663-1672• https://doi.org/10.1145/3357384.3357911E-commerce search engines are the primary means by which customers shop for products online. Each customer query contains multiple facets such as product type, color, brand, etc. A successful search engine retrieves products that are relevant to the ...
- research-article
A Sparse Topic Model for Extracting Aspect-Specific Summaries from Online Reviews
WWW '18: Proceedings of the 2018 World Wide Web Conference•April 2018, pp 1573-1582• https://doi.org/10.1145/3178876.3186069Online reviews have become an inevitable part of a consumer's decision making process, where the likelihood of purchase not only depends on the product's overall rating, but also on the description of its aspects. Therefore, e-commerce websites such as ...
