OpenRaiser/Intern-Atlas huggingface dataset:
PDAS: A Practical Distributed ADMM System for Large-Scale Linear Programming Problems at Alipay
Authors: Jun Zhou 0011, Yang Bao 0008, Daohong Jian, Hua Wu
Venue: KDD
Year: 2023

Linear programming (LP) is arguably the most common optimization problem encountered in practical settings. Important examples include machine learning systems optimization, resource allocation, and other decision-making scenarios. However, even with state-of-the-art (SOTA) solvers, it is extremely challenging to solve large-scale problems arising in industry settings, which could have up to billions of decision variables and require solutions within a time limit to meet business demands. This paper proposes PDAS, a Practical Distributed ADMM System to solve such problems with a variant of the Alternating Direction Method of Multipliers (ADMM) algorithm. PDAS offers user-friendly interfaces and provides near-linear speedup thanks to its high scalability and excellent performance. It also comes with a failover mechanism to ensure the stability of the iterative process. The convergence, feasibility, and optimality of PDAS have been verified on two real-world data-sets, resulting in a 10-4 average relative deviation from Gurobi. Although SOTA solvers do have advantages if only considering the solving time when tested on five small and medium-sized public data-sets, PDAS is more promising after including the modeling time. Moreover, when used to solve large-scale LP problems with up to 109 decision variables and 104 constraints in three real-world scenarios, PDAS achieves at least 2x speedups, well beyond the capabilities of SOTA.
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
Authors: Yin Zhang 0011, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi
Venue: KDD
Year: 2023

Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts recommender quality especially for the item slices without much user feedback. While there have been many research advances made in academia, deploying these methods in production is very difficult and very few improvements have been made in industry. One challenge is that these methods often hurt overall performance; additionally, they could be complex and expensive to train and serve.
Counterfactual Learning on Heterogeneous Graphs with Greedy Perturbation
Authors: Qiang Yang 0015, Changsheng Ma, Qiannan Zhang, Xin Gao 0001, Chuxu Zhang, Xiangliang Zhang 0001
Venue: KDD
Year: 2023

Due to the growing importance of using graph neural networks in high-stakes applications, there is a pressing need to interpret the predicted results of these models. Existing methods for explanation have mainly focused on generating sub-graphs comprising important edges for a specific prediction. However, these methods face two issues. Firstly, they lack counterfactual validity as removing the subgraph may not affect the prediction, and generating plausible counterfactual examples has not been adequately explored. Secondly, they cannot be extended to heterogeneous graphs as the complex information involved in such graphs increases the difficulty of generating interpretations. This paper proposes a novel counterfactual learning method, named CF-HGExplainer, for heterogeneous graphs. The method incorporates a semantic-aware attentive pooling strategy for the heterogeneous graph classifier and designs a heterogeneous decision boundaries extraction module to find the common logic for similar graphs based on the extracted graph embeddings from the classifier. Additionally, we propose to greedily perturb nodes and edges based on the distribution of node features and edge plausibility to train a neural network for heterogeneous edge weight learning. Extensive experiments on two public academic datasets demonstrate the effectiveness of CF-HGExplainer compared to state-of-the-art methods on the graph classification task and graph interpretation task.
Personalized Federated Learning with Parameter Propagation
Authors: Jun Wu 0019, Wenxuan Bao, Elizabeth A. Ainsworth, Jingrui He
Venue: KDD
Year: 2023

With decentralized data collected from diverse clients, a personalized federated learning paradigm has been proposed for training machine learning models without exchanging raw data from local clients. We dive into personalized federated learning from the perspective of privacy-preserving transfer learning, and identify the limitations of previous personalized federated learning algorithms. First, previous works suffer from negative knowledge transferability for some clients, when focusing more on the overall performance of all clients. Second, high communication costs are required to explicitly learn statistical task relatedness among clients. Third, it is computationally expensive to generalize the learned knowledge from experienced clients to new clients.
Spatio-temporal Diffusion Point Processes
Authors: Yuan Yuan 0032, Jingtao Ding, Chenyang Shao, Depeng Jin, Yong Li 0008
Venue: KDD
Year: 2023

Spatio-temporal point process (STPP) is a stochastic collection of events accompanied with time and space. Due to computational complexities, existing solutions for STPPs compromise with conditional independence between time and space, which consider the temporal and spatial distributions separately. The failure to model the joint distribution leads to limited capacities in characterizing the spatio-temporal entangled interactions given past events. In this work, we propose a novel parameterization framework for STPPs, which leverages diffusion models to learn complex spatio-temporal joint distributions. We decompose the learning of the target joint distribution into multiple steps, where each step can be faithfully described by a Gaussian distribution. To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively. For the first time, we break the restrictions on spatio-temporal dependencies in existing solutions, and enable a flexible and accurate modeling paradigm for STPPs. Extensive experiments from a wide range of fields, such as epidemiology, seismology, crime, and urban mobility, demonstrate that our framework outperforms the state-of-the-art baselines remarkably. Further in-depth analyses validate its ability to capture spatio-temporal interactions, which can learn adaptively for different scenarios. The datasets and source code are available online: https://github.com/tsinghua-fib-lab/Spatio-temporal-Diffusion-Point-Processes.
Towards Variance Reduction for Reinforcement Learning of Industrial Decision-making Tasks: A Bi-Critic based Demand-Constraint Decoupling Approach
Authors: Jianyong Yuan, Jiayi Zhang 0003, Zinuo Cai, Junchi Yan
Venue: KDD
Year: 2023

Learning to plan and schedule receives increasing attention due to its efficiency in problem-solving and potential to outperform heuristics. In particular, actor-critic-based reinforcement learning (RL) has been widely adopted for uncertain environments. Yet one standing challenge for applying RL to real-world industrial decision-making problems is the high variance during training. Existing efforts design novel value functions to alleviate the issue but still suffer. In this paper, we address this issue from the perspective of adjusting the actor-critic paradigm. We start by making an observation ignored in many industrial problems---the environmental dynamics for an agent consist of two parts physically independent of each other: the exogenous task demand over time and the hard constraint for action. And we theoretically show that decoupling these two effects in the actor-critic technique would reduce variance. Accordingly, we propose to decouple and model them separately in the state transition of the Markov decision process (MDP). In the demand-encoding process, the temporal task demand, e.g., the passengers for elevator scheduling is encoded followed by a critic for scoring. While in the constraint-encoding process, an actor-critic module is adopted for action embedding, and the two critics are then used for a revised advantaged function calculation. Experimental results show that our method can adaptively handle different dynamic planning and scheduling tasks and outperform recent learning-based models and traditional heuristic algorithms.
Quantifying Node Importance over Network Structural Stability
Authors: Fan Zhang 0036, Qingyuan Linghu, Jiadong Xie 0002, Kai Wang 0037, Xuemin Lin 0001, Wenjie Zhang 0001
Venue: KDD
Year: 2023

Quantifying node importance on engagement dynamics is critical to support network stability. We can motivate or retain the users in a social platform according to their importance s.t. the network is more sustainable. Existing studies validate that the coreness of a node is the "best practice" on network topology to estimate the engagement of the node. In this paper, the importance of a node is the effect on the engagement of other nodes when its engagement is strengthened or weakened. Specifically, the importance of a node is quantified via two novel concepts: the anchor power to measure the engagement effect of node strengthening (i.e., the overall coreness gain) and the collapse power to measure the engagement effect of node weakening (i.e., the overall coreness loss). We find the computation of the two concepts can be naturally integrated into a shell component-based framework, and propose a unified static algorithm to compute both the anchored and collapsed followers. For evolving networks, efficient maintenance techniques are designed to update the follower sets of each node, which is faster than redoing the static algorithm by around 3 orders of magnitude. Extensive experiments on real-life data demonstrate the effectiveness of our model and the efficiency of our algorithms.
Exploiting Intent Evolution in E-commercial Query Recommendation
Authors: Yu Wang 0158, Zhengyang Wang, Hengrui Zhang, Qingyu Yin, Xianfeng Tang, Yinghan Wang, Danqing Zhang, Limeng Cui, Monica Xiao Cheng, Bing Yin, Suhang Wang, Philip S. Yu
Venue: KDD
Year: 2023

Aiming at a better understanding of the search goals in the user search sessions, recent query recommender systems explicitly model the reformulations of queries, which hopes to estimate the intents behind these reformulations and thus benefit the next-query recommendation. However, in real-world e-commercial search scenarios, user intents are much more complicated and may evolve dynamically. Existing methods merely consider trivial reformulation intents from semantic aspects and fail to model dynamic reformulation intent flows in search sessions, leading to sub-optimal capacities to recommend desired queries. To deal with these limitations, we first explicitly define six types of query reformulation intents according to the desired products of two consecutive queries. We then apply two self-attentive encoders on top of two pre-trained large language models to learn the transition dynamics from semantic query and intent reformulation sequences, respectively. We develop an intent-aware query decoder to utilize the predicted intents for suggesting the next queries. We instantiate such a framework with an Intent-aware Variational AutoEncoder (IVAE) under deployment at Amazon. We conduct comprehensive experiments on two real-world e-commercial datasets from Amazon and one public dataset from BestBuy. Specifically, IVAE improves the Recall@15 by 25.44% and 60.47% on two Amazon datasets and 13.91% on BestBuy, respectively.
epiDAMIK 6.0: The 6th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery
Authors: Bijaya Adhikari, Alexander Rodríguez, Amulya Yadav, Sen Pei, Ajitesh Srivastava, Marie-Laure Charpignon, Anil Vullikanti, B. Aditya Prakash
Venue: KDD
Year: 2023

The epiDAMIK workshop serves as a platform for advancing the utilization of data-driven methods in the fields of epidemiology and public health research. These fields have seen relatively limited exploration of data-driven approaches compared to other disciplines. Therefore, our primary objective is to foster the growth and recognition of the emerging discipline of data-driven and computational epidemiology, providing a valuable avenue for sharing state-of-the-art research and ongoing projects. The workshop also seeks to showcase results that are not typically presented at major computing conferences, including valuable insights gained from practical experiences. Our target audience encompasses researchers in AI, machine learning, and data science from both academia and industry, who have a keen interest in applying their work to epidemiological and public health contexts. Additionally, we welcome practitioners from mathematical epidemiology and public health, as their expertise and contributions greatly enrich the discussions. Homepage: https://epidamik.github.io/
KDD Workshop on Machine Learning in Finance
Authors: Leman Akoglu, Nitesh V. Chawla, Senthil Kumar, Saurabh Nagrecha, Mahashweta Das, Vidyut M. Naware, Tanveer A. Faruquie
Venue: KDD
Year: 2023

The finance industry is constantly faced with an ever evolving set of challenges including credit card fraud, identity theft, network intrusion, money laundering, human trafficking, and illegal sales of firearms. There is also the newly emerging threat of fake news in financial media that can lead to distortions in trading strategies and investment decisions. In addition, traditional problems such as customer analytics, forecasting, and recommendations take on a unique flavor when applied to financial data. A number of new ideas are emerging to tackle all these problems including self-supervised learning methods, deep learning algorithms, network/graph based solutions as well as linguistic approaches. These methods must often be able to work in real-time and be able handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss both the problems faced by the financial industry and potential solutions. We plan to invite regular papers, positional papers and extended abstracts of work in progress. We will also encourage short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers.
Fair Allocation Over Time, with Applications to Content Moderation
Authors: Amine Allouah, Christian Kroer, Xuan Zhang, Vashist Avadhanula, Nona Bohanon, Anil Dania, Caner Gocmen, Sergey Pupyrev, Parikshit Shah, Nicolás Stier Moses, Ken Rodríguez Taarup
Venue: KDD
Year: 2023

In today's digital world, interaction with online platforms is ubiquitous, and thus content moderation is important for protecting users from content that do not comply with pre-established community guidelines. Given the vast volume of content generated online daily, having an efficient content moderation system throughout every stage of planning is particularly important. We study the short-term planning problem of allocating human content reviewers to different harmful content categories. We use tools from fair division and study the application of competitive equilibrium and leximin allocation rules for addressing this problem. On top of the traditional Fisher market setup, we additionally incorporate novel aspects that are of practical importance. The first aspect is the forecasted workload of different content categories, which puts constraints on the allocation chosen by the planner. We show how a formulation that is inspired by the celebrated Eisenberg-Gale program allows us to find an allocation that not only satisfies the forecasted workload, but also fairly allocates the remaining working hours from the content reviewers among all content categories. A fair allocation of oversupply provides a guardrail in cases where the actual workload deviates from the predicted workload. The second practical consideration is time dependent allocation that is motivated by the fact that partners need scheduling guidance for the reviewers across days to achieve efficiency. To address the time component, we introduce new extensions of the various fair allocation approaches for the single-time period setting, and we show that many properties extend in essence, albeit with some modifications. Lastly, related to the time component, we additionally investigate how to satisfy markets' desire for smooth allocation (i.e, an allocation that does not vary much from time to time) so that the switch in staffing is minimized. We demonstrate the performance of our proposed approaches through real-world data obtained from Meta.
NFT-Based Data Marketplace with Digital Watermarking
Authors: Saeed Ranjbar Alvar, Mohammad Akbari, David (Ming Xuan) Yue, Yong Zhang 0004
Venue: KDD
Year: 2023

In today's digital world, enterprises and individuals are generating massive data that is potentially useful for many data consumers with data driven applications. The emergence of data marketplaces is a step toward helping the data owners to monetize their digital assets and get connected to the potential buyers. The current data marketplaces cannot handle the challenges related to data ownership claims, illegal redistribution, and data ownership traceability. To overcome these problems in a general-purpose market, we propose a marketplace based on watermarking and Non-Fungible Token (NFT) technologies. In the proposed NFT-based marketplace, the owner's data is stored as an NFT where the underlying content of the NFT holds the watermarked data. The watermarked data is obtained by embedding some information about the owners and the buyers into the original data. The embedded information can later be extracted to identify the owner and the buyer of the traded data. Furthermore, the transactions corresponding to the NFT provide verifiable ownership proof and traceable ownership history. A Proof-Of-Concept (POC) implementation of the proposed marketplace that will be integrated within AI-Gallery Data Marketplace service in Huawei Cloud is presented for trading image data. An extensive set of experiments to measure the gas consumption on the blockchain and evaluate the robustness of the watermarked assets against 51 attacks are performed. Finally, a method based on error correction codes is proposed for improving the watermarking robustness in the implemented marketplace. The link for the codes and the POC demo is provided in the appendix.
CADENCE: Offline Category Constrained and Diverse Query Generation for E-commerce Autosuggest
Authors: Abhinav Anand, Surender Kumar, Nandeesh Kumar, Samir Shah
Venue: KDD
Year: 2023

Query AutoComplete (QAC) or AutoSuggest is the first place of user interaction with an e-commerce search engine. It is critical for the QAC system to suggest relevant and well-formed queries for multiple possible user intents. Suggesting only the historical user queries fails in the case of infrequent or new prefixes. Much of the recent works generate synthetic candidates using models trained on user queries and thus have these issues: a) cold start problem as new products in the catalogue fail to get visibility due to lack of representation in user queries b) poor quality of generated candidates due to concept drift and c) low diversity/coverage of attributes such as brand, color & other facets in generated candidates.
Learning to Solve Grouped 2D Bin Packing Problems in the Manufacturing Industry
Authors: Wenxuan Ao, Guozhen Zhang, Yong Li 0008, Depeng Jin
Venue: KDD
Year: 2023

The two-dimensional bin packing problem (2DBP) is a critical optimization problem in the furniture production and glass cutting industries, where the objective is to cut smaller-sized items from a minimum number of large standard-sized raw materials. In practice, factories manufacture hundreds of customer orders (sets of items) every day, and to relieve pressure in management, a common practice is to group the orders into batches for production, ensuring that items from one order are in the same batch instead of scattered across the production line. In this work, we formulate this problem as the grouped 2D bin packing problem, a bi-level problem where the upper level partitions orders into groups and the lower level solves 2DBP for items in each group. The main challenges are (1) the coupled optimization of upper and lower levels and (2) the high computational efficiency required for practical application. To tackle these challenges, we propose an iteration-based hierarchical reinforcement learning framework, which can learn to solve the optimization problem in a data-driven way and provide fast online performance after offline training. Extensive experiments demonstrate that our method not only achieves the best performance compared to all baselines but is also robust to changes in dataset distribution and problem constraints. Finally, we deployed our method in the ARROW Home factory in China, resulting in a 4.1% reduction in raw material costs. We have released the source code and datasets to facilitate future research.
AdKDD 2023
Authors: Abraham Bagherjeiran, Nemanja Djuric, Kuang-Chih Lee, Linsey Pang, Vladan Radosavljevic, Suju Rajan
Venue: KDD
Year: 2023

The digital advertising field has always had challenging ML problems, learning from petabytes of data that is highly imbalanced, reactivity times in the milliseconds, and more recently compounded with the complex user's path to purchase across devices, across platforms, and even online/real-world behavior. The AdKDD workshop continues to be a forum for researchers in advertising, during and after KDD. Our website which hosts slides and abstracts receives approximately 2,000 monthly visits and 1,800 active users during the KDD 2021. In surveys during AdKDD 2019 and 2020, over 60% agreed that AdKDD is the reason they attended KDD, and over 90% indicated they would attend next year. The 2023 edition is particularly timely because of the increasing application of Graph-based NN and Generative AI models in advertising. Coupled with privacy-preserving initiatives enforced by GDPR, CCPA the future of computational advertising is at an interesting crossroads. For this edition, we plan to solicit papers that span the spectrum of deep user understanding while remaining privacy-preserving. In addition, we will seek papers that discuss fairness in the context of advertising, to what extent does hyper-personalization work, and whether the ad industry as a whole needs to think through more effective business models such as incrementality. We have hosted several academic and industry luminaries as keynote speakers and have found our invited speaker series hosting expert practitioners to be an audience favorite. We will continue fielding a diverse set of keynote speakers and invited talks for this edition as well. As with past editions, we hope to motivate researchers in this space to think not only about the ML aspects but also to spark conversations about the societal impact of online advertising.
Communication Efficient and Differentially Private Logistic Regression under the Distributed Setting
Authors: Ergute Bao, Dawei Gao, Xiaokui Xiao, Yaliang Li
Venue: KDD
Year: 2023

We study the classic machine learning problem of logistic regression with differential privacy (DP), under the distributed setting. While logistic regression with DP has been extensively studied in the literature, most of the research is focused on the centralized setting, where a centralized server is trusted with the entire private training dataset. However, in many real-world scenarios (e.g., federated learning), the data is distributed among multiple clients who may not trust others, including clients and the server. While the server tries to learn a model using the clients' private datasets, the clients should provide each individual record in their local datasets with a formal privacy guarantee.