OpenRaiser/Intern-Atlas huggingface dataset:
CERAM: Coverage Expansion for Recommendations by Associating Discarded Models
Authors: Yoshiki Matsune, Kota Tsubouchi, Nobuhiko Nishio
Venue: KDD
Year: 2022

Systems that utilize and manage predictive models have become increasingly significant in industry. In the services offered by Yahoo! JAPAN, once a predictive model is utilized for recommendations, it is thrown away. Such models could however be reused for expanding the coverage of other recommendations. Here, our goal is to construct recommendation systems that expand the coverage of recommendations by effectively utilizing models which would otherwise be discarded. Another goal is to deploy such a recommendation system on real services and make practical use of it. In this paper, we describe a recommendation system that achieves these two goals by overcoming the challenges facing its deployment on real services. Specifically, we developed an optimization method that alleviates the psychological barrier against using the recommendation system and clarified the performance of our method in making real recommendations. An offline test and a large-scale online test on making real recommendations showed that our method substantially expands the coverage of recommendations. As a highlight of the results, our method made recommendations to 76.9 times more users at the same level of recommendation performance as the currently used recommendation system by the service. Overall, the results show that our method has a huge impact on services and can be applied to real recommendations.
Learning Models of Individual Behavior in Chess
Authors: Reid McIlroy-Young, Russell Wang, Siddhartha Sen 0001, Jon M. Kleinberg, Ashton Anderson
Venue: KDD
Year: 2022

AI systems that can capture human-like behavior are becoming increasingly useful in situations where humans may want to learn from these systems, collaborate with them, or engage with them as partners for an extended duration. In order to develop human-oriented AI systems, the problem of predicting human actions---as opposed to predicting optimal actions---has received considerable attention. Existing work has focused on capturing human behavior in an aggregate sense, which potentially limits the benefit any particular individual could gain from interaction with these systems. We extend this line of work by developing highly accurate predictive models of individual human behavior in chess. Chess is a rich domain for exploring human-AI interaction because it combines a unique set of properties: AI systems achieved superhuman performance many years ago, and yet humans still interact with them closely, both as opponents and as preparation tools, and there is an enormous corpus of recorded data on individual player games. Starting with Maia, an open-source version of AlphaZero trained on a population of human players, we demonstrate that we can significantly improve prediction accuracy of a particular player's moves by applying a series of fine-tuning methods. Furthermore, our personalized models can be used to perform stylometry---predicting who made a given set of moves---indicating that they capture human decision-making at an individual level. Our work demonstrates a way to bring AI systems into better alignment with the behavior of individual people, which could lead to large improvements in human-AI interaction.
The 11th International Workshop on Urban Computing
Authors: Chuishi Meng, Yanhua Li, Yu Zheng 0004, Jieping Ye, Qiang Yang 0001, Philip S. Yu, Ouri Wolfson
Venue: KDD
Year: 2022

Urbanization's rapid progress has led to many big cities, which have modernized many people's lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges were nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g., human mobility, air quality, traffic patterns, and geographical data. Motivated by the opportunities of building more intelligent cities, we came up with a vision of urban computing, which aims to unlock the power of knowledge from big and heterogeneous data collected in urban spaces and apply this powerful information to solve major issues our cities face today. This is the eleventh time that we organize this workshop. The previous 10 workshops were hosted with SIGKDD and SIGSPATIAL, each of which attracted over 70 participants and 30 submissions on average.
Graph Neural Network Training and Data Tiering
Authors: Seungwon Min, Kun Wu 0002, Mert Hidayetoglu, Jinjun Xiong, Xiang Song 0003, Wen-Mei Hwu
Venue: KDD
Year: 2022

Graph Neural Networks (GNNs) have shown success in learning from graph-structured data, with applications to fraud detection, recommendation, and knowledge graph reasoning. However, training GNN efficiently is challenging because: 1) GPU memory capacity is limited and can be insufficient for large datasets, and 2) the graph-based data structure causes irregular data access patterns. In this work, we provide a method to statistically analyze and identify more frequently accessed data ahead of GNN training. Our data tiering method not only utilizes the structure of input graph, but also an insight gained from actual GNN training process to achieve a higher prediction result. With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead. We also take into account of multi-GPU GNN training as well and we demonstrate the effectiveness of our strategy in a multi-GPU system. The evaluation results show that our work reduces CPU-GPU traffic by 87-95% and improves the training speed of GNN over the existing solutions by 1.6-2.1x on graphs with hundreds of millions of nodes and billions of edges.
Towards Reliable Detection of Dielectric Hotspots in Thermal Images of the Underground Distribution Network
Authors: François Mirallès, Luc Cauchon, Marc-André Magnan, François Grégoire, Mouhamadou Makhtar Dione, Arnaud Zinflou
Venue: KDD
Year: 2022

This paper introduces a thermographic vision system to detect different types of hotspots on a variety of cable junctions commonly found in Hydro-Québec underground electrical distribution network. Cable junctions of underground distribution networks operate in harsh conditions, potentially leading to failure overtime. Faults can be prevented by the timely detection of local hotspot on these junctions. Hotspot detection is carried out by mean of image segmentation using a deep neural network. Special care is given to uncertainty estimation and validation. Uncertainty is used to assess the quality of a segmentation to avoid misdiagnosis or returning in the field to recapture images. It is also proposed as a tool to evaluate whether unannotated images should be included in the dataset. System performance has been evaluated on a test dataset as well as in the field by regular inspection teams. Promising results obtained so far led to the deployment of the vision system on a fleet of five inspection trucks performing inspection over the province over the last year Authorization was granted to scale the solution to 35 trucks starting this year.
Minimizing Congestion for Balanced Dominators
Authors: Yosuke Mizutani, Annie Staker, Blair D. Sullivan
Venue: KDD
Year: 2022

A primary challenge in metagenomics is reconstructing individual microbial genomes from the mixture of short fragments created by sequencing. Recent work leverages the sparsity of the assembly graph to find r-dominating sets which enable rapid approximate queries through a dominator-centric graph partition. In this paper, we consider two problems related to reducing uncertainty and improving scalability in this setting.
Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation
Authors: Ali Montazeralghaem, James Allan 0001
Venue: KDD
Year: 2022

Conversational search and recommendation systems can ask clarifying questions through the conversation and collect valuable information from users. However, an important question remains: how can we extract relevant information from the user's utterances and use it in the retrieval or recommendation in the next turn of the conversation? Utilizing relevant information from users' utterances leads the system to better results at the end of the conversation. In this paper, we propose a model based on reinforcement learning, namely RelInCo, which takes the user's utterances and the context of the conversation and classifies each word in the user's utterances as belonging to the relevant or non-relevant class. RelInCo uses two Actors: 1) Arrangement-Actor, which finds the most relevant order of words in user's utterances, and 2) Selector-Actor, which determines which words, in the order provided by the arrangement Actor, can bring the system closer to the target of the conversation. In this way, we can find relevant information in the user's utterance and use it in the conversation. The objective function in our model is designed in such a way that it can maximize any desired retrieval and recommendation metrics (i.e., the ultimate
Online Clustering: Algorithms, Evaluation, Metrics, Applications and Benchmarking
Authors: Jacob Montiel, Hoang-Anh Ngo, Minh-Huong Le Nguyen, Albert Bifet
Venue: KDD
Year: 2022

Online clustering algorithms play a critical role in data science, especially with the advantages regarding time, memory usage and complexity, while maintaining a high performance compared to traditional clustering methods. This tutorial serves, first, as a survey on online machine learning and, in particular, data stream clustering methods. During this tutorial, state-of-the-art algorithms and the associated core research threads will be presented by identifying different categories based on distance, density grids and hidden statistical models. Clustering validity indices, an important part of the clustering process which are usually neglected or replaced with classification metrics, resulting in misleading interpretation of final results, will also be deeply investigated.
DNA-Stabilized Silver Nanocluster Design via Regularized Variational Autoencoders
Authors: Fariha Moomtaheen, Matthew Killeen, James T. Oswald, Anna Gonzàlez-Rosell, Peter Mastracco, Alexander Gorovits, Stacy M. Copp, Petko Bogdanov
Venue: KDD
Year: 2022

DNA-stabilized silver nanoclusters (AgN-DNAs) are a class of nanomaterials comprised of 10-30 silver atoms held together by short synthetic DNA template strands. AgN-DNAs are promising biosensors and fluorophores due to their small sizes, natural compatibility with DNA, and bright fluorescence---the property of absorbing light and re-emitting light of a different color. The sequence of the DNA template acts as a "genome" for AgN-DNAs, tuning the size of the encapsulated silver nanocluster, and thus its fluorescence color. However, current understanding of the AgN-DNA genome is still limited. Only a minority of DNA sequences produce highly fluorescent AgN-DNAs, and the bulky DNA strands and complex DNA-silver interactions make it challenging to use first principles chemical calculations to understand and design AgN-DNAs. Thus, a major challenge for researchers studying these nanomaterials is to develop methods to employ observational data about studied AgN-DNAs to design new nanoclusters for targeted applications.
Solar: Science of Entity Loss Attribution
Authors: Anshuman Mourya, Prateek Sircar, Anirban Majumder, Deepak Gupta
Venue: KDD
Year: 2022

The ability to accurately pinpoint the location of an event (e.g. loss, fault or bug) is of fundamental requirement in many systems. While we have state-of-the-art models to predict likelihood of an outcome, being able to pinpoint to the entity responsible for the outcome is also important. For example, in an e-commerce setup, a lost package detection system needs to infer the reason or location (delivery station, sort center, trucks) in case of a missing item, a network management system would like to diagnose nodes that are faulty based on end-end packet flow traces or a compiler needs to point out the exact location of a code that is erroneous. In this paper, we present an Attention based neural architecture for entity localization to accurately pinpoint the location of package loss in delivery network and bugs in erroneous programs. Our model performs well in scenarios where there is no annotation/ground truth for entities for localization. It can also adapt itself if annotations/ground truth is available for even a subset of entities by leveraging semi-supervision. The core of our model is a ladder-style architecture that helps us achieve state-of-the-art performance in both entity localization and detection. Further, to show the generality of our approach, we demonstrate its performance on a bug localization task for software programs. On a publicly available data-set, our solution outperforms the state-of-the-art technique by a significant margin.
Pricing the Long Tail by Explainable Product Aggregation and Monotonic Bandits
Authors: Marco Mussi, Gianmarco Genalti, Francesco Trovò, Alessandro Nuara, Nicola Gatti 0001, Marcello Restelli
Venue: KDD
Year: 2022

In several e-commerce scenarios, pricing long-tail products effectively is a central task for the companies, and there is broad agreement that Artificial Intelligence (AI) will play a prominent role in doing that in the next future. Nevertheless, dealing with long-tail products raises major open technical issues due to data scarcity which preclude the adoption of the mainstream approaches requiring usually a huge amount of data, such as, e.g., deep learning. In this paper, we provide a novel online learning algorithm for dynamic pricing that deals with non-stationary settings due to, e.g., the seasonality or adaptive competitors, and is very efficient in terms of the need for data thanks to assumptions such as, e.g., the monotonicity of the demand curve in the price that are customarily satisfied in long-tail markets. Furthermore, our dynamic pricing algorithm is paired with a clustering algorithm for the long-tail products which aggregates similar products such that the data of all the products of the same cluster are merged and used to choose their best price. We first evaluate our algorithms in an offline synthetic setting, comparing their performance with the state of the art and showing that our algorithms are more robust and data-efficient in long-tail settings. Subsequently, we evaluate our algorithms in an online setting with more than 8,000 products, including popular and long-tail, in an A/B test with humans for about two months. The increase of revenue thanks to our algorithms is about 18% for the popular products and about 90% for the long-tail products.
Nonlinearity Encoding for Extrapolation of Neural Networks
Authors: Gyoung S. Na, Chanyoung Park 0001
Venue: KDD
Year: 2022

Extrapolation to predict unseen data outside the training distribution is a common challenge in real-world scientific applications of physics and chemistry. However, the extrapolation capabilities of neural networks have not been extensively studied in machine learning. Although it has been recently revealed that neural networks become linear regression in extrapolation problems, a universally applicable method to support the extrapolation of neural networks in general regression settings has not been investigated. In this paper, we propose automated nonlinearity encoder (ANE) that is a data-agnostic embedding method to improve the extrapolation capabilities of neural networks by conversely linearizing the original input-to-target relationships without architectural modifications of prediction models. ANE achieved state-of-the-art extrapolation accuracies in extensive scientific applications of various data formats. As a real-world application, we applied ANE for high-throughput screening to discover novel solar cell materials, and ANE significantly improved the screening accuracy.
First Workshop on Content Understanding and Generation for E-commerce
Authors: Sumit Negi, Manisha Verma, Rajdeep H. Banerjee, Pooja A, Lydia B. Chilton, Mithun Das Gupta, Vinay P. Namboodiri, Dinesh Garg
Venue: KDD
Year: 2022

Shopping experience on any e-commerce website is largely driven by the content customers interact with. The large volume of diverse content on e-commerce platforms, and the advances in machine learning, pose unique opportunities for gathering insights through content understanding and applying these insights to generate content better shopper experience. The purpose of the first edition of this workshop was to bring together researchers from industry and academia on questions surrounding e-commerce content understanding and generation.
DI-2022: The Third Document Intelligence Workshop
Authors: Ani Nenkova, Douglas Burdick, Benjamin Han, Dave Lewis 0003, Sandeep Tata, Dan Tecuci
Venue: KDD
Year: 2022

Business documents are central to the operation of all organizations, and they come in all shapes and sizes: project reports, planning documents, technical specifications, financial statements, meeting minutes, legal agreements, contracts, resumes, purchase orders, invoices, and many more. The ability to read, understand and interpret these documents, referred to here as Document Intelligence (DI), is challenging due to not only many domains of knowledge involved, but also their complex formats and structures, internal and external cross references deployed, and even less-than-ideal quality of scans and OCR oftentimes performed on them. This workshop aims to explore and advance the current state of research and practice in answering these challenges.
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
Authors: Alexander V. Nikitin, Samuel Kaski
Venue: KDD
Year: 2022

Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers). Our system interacts with domain experts to improve predictions and elicit their knowledge. In our approach, domain experts are included in the loop not only as providers of correct labels, as in traditional active learning, but as a source of explicit decision rule feedback. The system is automated and designed to be easily extended to novel domains, such as maintaining workstations of several organizations. In addition, we develop a simulator for reproducible experiments in a controlled environment and deploy the system in a large-scale case of real-life workstations PdM with thousands of workstations for dozens of companies.
FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy
Authors: Qiying Pan, Yifei Zhu 0001
Venue: KDD
Year: 2022

Node embedding aims to map nodes in the complex graph into low-dimensional representations. The real-world large-scale graphs and difficulties of labeling motivate wide studies of unsupervised node embedding problems. Nevertheless, previous effort mostly operates in a centralized setting where a complete graph is given. With the growing awareness of data privacy, data holders who can be represented by one vertex in the graph and are only its neighbors demand greater privacy protection. In this paper, we introduce FedWalk, a random-walk-based unsupervised node embedding algorithm that operates in such a node-level visibility graph with raw graph information remaining locally. FedWalk is designed to offer centralized competitive graph representation capability with data privacy protection and great communication efficiency. FedWalk instantiates the prevalent federated paradigm and contains three modules. We first design a hierarchical clustering tree (HCT) constructor to extract the structural feature of each node. A dynamic time warping algorithm seamlessly handles the structural heterogeneity across different nodes. Based on the constructed HCT, we then design a random walk generator, wherein a sequence encoder is designed to preserve privacy and a two-hop neighbor predictor is designed to save communication cost. The generated random walks are then used to update node embedding based on a SkipGram model. Extensive experiments on two large graphs demonstrate that FedWalk achieves competitive representativeness as a centralized node embedding algorithm does with only up to 1.8% Micro-F1 score and 4.4% Marco-F1 score loss while reducing about 6.7 times of inter-device communication per walk.
Deep Search Relevance Ranking in Practice
Authors: Linsey Pang, Wei Liu 0007, Keng-hao Chang, Xue Li, Moumita Bhattacharya, Xianjing Liu, Stephen D. Guo
Venue: KDD
Year: 2022

Machine learning techniques for developing industry-scale search engines have long been a prominent part of most domains and their online products. Search relevance algorithms are key components of products across different fields, including e-commerce, streaming services, and social networks. In this tutorial, we give an introduction to such large-scale search ranking systems, specifically focusing on deep learning techniques in this area. The topics we cover are the following: (1) Overview of search ranking systems in practice, including classical and machine learning techniques; (2) Introduction to sequential and language models in the context of search ranking; and (3) Knowledge distillation approaches for this area. For each of the aforementioned sessions, we first give an introductory talk and then go over an hands-on tutorial to really hone in on the concepts. We cover fundamental concepts using demos, case studies, and hands-on examples, including the latest Deep Learning methods that have achieved state-of-the-art results in generating the most relevant search results. Moreover, we show example implementations of these methods in python, leveraging a variety of open-source machine-learning/deep-learning libraries as well as real industrial data or open-source data.
ANDEA: Anomaly and Novelty Detection, Explanation, and Accommodation
Authors: Guansong Pang, Jundong Li, Anton van den Hengel, Longbing Cao, Thomas G. Dietterich
Venue: KDD
Year: 2022

The detection of, explanation of, and accommodation to anomalies and novelties are active research areas in multiple communities, including data mining, machine learning, and computer vision. They are applied in various guises including anomaly detection, out-of-distribution example detection, adversarial example recognition and detection, curiosity-driven reinforcement learning, and open-set recognition and adaptation, all of which are of great interest to the SIGKDD community. The techniques developed have been applied in a wide range of domains including fraud detection and anti-money laundering in fintech, early disease detection, intrusion detection in large-scale computer networks and data centers, defending AI systems from adversarial attacks, and in improving the practicality of agents through overcoming the closed-world assumption.
Advances in Exploratory Data Analysis, Visualisation and Quality for Data Centric AI Systems
Authors: Hima Patel, Shanmukha C. Guttula, Ruhi Sharma Mittal, Naresh Manwani, Laure Berti-Équille, Abhijit Manatkar
Venue: KDD
Year: 2022

It is widely accepted that data preparation is one of the most time-consuming steps of the machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of data directly influences the quality of a model. In this tutorial, we will discuss the importance and the role of exploratory data analysis (EDA) and data visualisation techniques to find data quality issues and for data preparation, relevant to building ML pipelines. We will also discuss the latest advances in these fields and bring out areas that need innovation. To make the tutorial actionable for practitioners, we will also discuss the most popular open-source packages that one can get started with along with their strengths and weaknesses. Finally, we will discuss on the challenges posed by industry workloads and the gaps to be addressed to make data-centric AI real in industry settings.