OpenRaiser/Intern-Atlas huggingface dataset:
Cluster-Aware Heterogeneous Information Network Embedding
Authors: Rayyan Ahmad Khan, Martin Kleinsteuber
Venue: WSDM
Year: 2022

Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. HIN embedding has emerged as a promising research field for network analysis as it enables downstream tasks such as clustering and node classification. In this work, we propose VaCA-HINE for joint learning of cluster embeddings as well as cluster-aware HIN embedding. We assume that the connected nodes are highly likely to fall in the same cluster, and adopt a variational approach to preserve the information in the pairwise relations in a cluster-aware manner. In addition, we deploy contrastive modules to simultaneously utilize the information in multiple meta-paths, thereby alleviating the meta-path selection problem - a challenge faced by many of the famous HIN embedding approaches. The HIN embedding, thus learned, not only improves the clustering performance but also preserves pairwise proximity as well as the high-order HIN structure. We show the effectiveness of our approach by comparing it with many competitive baselines on three real-world datasets on clustering and downstream node classification.
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
Authors: Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto
Venue: WSDM
Year: 2022

In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking policies is thus gaining a growing interest because it enables performance estimation of new ranking policies using only logged data. Although OPE in contextual bandits has been studied extensively, its naive application to the ranking setting faces a critical variance issue due to the huge item space. To tackle this problem, previous studies introduce some assumptions on user behavior to make the combinatorial item space tractable. However, an unrealistic assumption may, in turn, cause serious bias. Therefore, appropriately controlling the bias-variance tradeoff by imposing a reasonable assumption is the key for success in OPE of ranking policies. To achieve a well-balanced bias-variance tradeoff, we propose the Cascade Doubly Robust estimator building on the cascade assumption, which assumes that a user interacts with items sequentially from the top position in a ranking. We show that the proposed estimator is unbiased in more cases compared to existing estimators that make stronger assumptions on user behavior. Furthermore, compared to a previous estimator based on the same cascade assumption, the proposed estimator reduces the variance by leveraging a control variate. Comprehensive experiments on both synthetic and real-world e-commerce data demonstrate that our estimator leads to more accurate OPE than existing estimators in a variety of settings.
Multi-Resolution Attention for Personalized Item Search
Authors: Furkan Kocayusufoglu, Tao Wu, Anima Singh, Georgios Roumpos, Heng-Tze Cheng, Sagar Jain, Ed H. Chi, Ambuj K. Singh
Venue: WSDM
Year: 2022

Personalized item search has become an essential tool for online platforms---where users interact with a large corpus of items (e.g., click, purchase, like) via a search query---to provide their users with a more satisfactory search experience. The record (or history) of users' past interactions serves as a valuable asset to achieve personalization. While user history data can span over a long period of time, only a part of the history is relevant to a user's current search intent. Moreover, since historical interactions take place at aperiodic points in time, modeling their relevance to the current search query entangles complex temporal dependencies. We propose multi-resolution attention to address these challenges for personalized item search. Our approach captures higher-order temporal relations between user queries and their history across several temporal subspaces (i.e., resolutions), each corresponding to distinct temporal ranges with adaptive time boundaries that are also learned directly from data. We achieve this by coupling the conventional multi-head attention module with a differentiable soft-thresholding mechanism, which essentially operates as a masking function in the temporal domain. Comparisons with strong baselines on an open-source benchmark dataset confirm the efficacy of our approach.
The Pit Stop Problem: How to Plan Your Next Road Trip
Authors: Kostas Kollias
Venue: WSDM
Year: 2022

Many online trip planning and navigation software need to routinely solve the problem of deciding where to take stops during a journey for various services such as refueling (or EV charging), rest stops, food, etc. The goal is to minimize the overhead of these stops while ensuring that the traveler is not starved of any essential resource (such as fuel, rest, or food) during the journey. In this paper, we formally model this problem and call it the pit stop problem. We design algorithms for this problem under various settings: single vs multiple types of stops, and offline vs online optimization (i.e., in advance of or during the trip). Our algorithms achieve provable guarantees in terms of approximating the optimal solution. We then extensively evaluate our algorithms on real world data and demonstrate that they significantly outperform baseline solutions.
Efficient Two-stage Label Noise Reduction for Retrieval-based Tasks
Authors: Mengmeng Kuang, Weiyan Wang, Zhenhong Chen, Lie Kang, Qiang Yan
Venue: WSDM
Year: 2022

The existence of noisy labels in datasets has always been an essential dilemma in deep learning studies. Previous works detected noisy labels by analyzing the predicted probability distribution generated by the model trained on the same data and calculating the probabilities of each label to be regarded as noise. However, the predicted probability distribution from the whole dataset may introduce overfitting, and the overfitting on noisy labels may induce the probability distribution of clean and noisy items to be not conditional independent, making identification more challenging. Additionally, label noise reduction on image datasets has received much attention, while label noise reduction on text datasets has not. This paper proposes a noisy label reduction method for text datasets, which could be applied at retrieval-based tasks by getting a conditional independent probability distribution to identify noisy labels accurately. The method first generates a candidate set containing noisy labels, predicts the category probabilities by the model trained on the rest cleaner data, and then identifies noisy items by analyzing a confidence matrix. Moreover, we introduce a warm-up module and a sharpened cross-entropy loss function for efficiently training in the first stage. Empirical results on different rates of uniform and random label noise in five text datasets demonstrate that our method can improve the label noise reduction accuracy and end-to-end classification accuracy. Further, we find that the iteration of the label noise reduction method is efficient to high-rate label noise datasets, and our method will not hurt clean datasets too much.
Differential Query Semantic Analysis: Discovery of Explicit Interpretable Knowledge from E-Com Search Logs
Authors: Sahiti Labhishetty, ChengXiang Zhai, Min Xie, Lin Gong, Rahul Sharnagat, Satya Chembolu
Venue: WSDM
Year: 2022

We present a novel strategy for analyzing E-Com search logs called Differential Query Semantic Analysis (DQSA) to discover explicit interpretable knowledge from search logs in the form of a semantic lexicon that makes context-specific mapping from a query segment (word or phrase) to the preferred attribute values of a product. Evaluation on a set of size-related query segments and attribute values shows that DQSA can effectively discover meaningful mappings of size-related query segments to their preferred specific attributes and attributes values in the context of a product type. DQSA has many uses including improvement of E-Com search accuracy by bridging the vocabulary gap, comparative analysis of search intent, and alleviation of the problem of tail queries and products.
Collaborative Curating for Discovery and Expansion of Visual Clusters
Authors: Dung D. Le, Hady W. Lauw
Venue: WSDM
Year: 2022

In many visually-oriented applications, users can select and group images that they find interesting into coherent clusters. For instance, we encounter these in the form of hashtags on Instagram, galleries on Flickr, or boards on Pinterest. The selection and coherence of such user-curated visual clusters arise from a user's preference for a certain type of content as well as her own perception of which images are similar and thus belong to a cluster. We seek to model such curation behaviors towards supporting users in their future activities such as expanding existing clusters or discovering new clusters altogether. This paper proposes a framework, namely COLLABORATIVE CURATING that jointly models the interrelated modalities of preference expression and similarity perception. Extensive experiments on real-world datasets from a visual curating platform show that the proposed framework significantly outperforms baselines focusing on either clustering behaviors or preferences alone.
Towards Practical Robustness Evaluation and Robustness Enhancing
Authors: Yaxin Li
Venue: WSDM
Year: 2022

Deep neural networks (DNNs) have been widely applied on various machine learning tasks and have achieved significant performance across multiple domains. However, it well known that DNNs suffer from severe adversarial vulnerability. Thus it raises great concernswhen DNNs are adopted to safety-critical tasks. These concerns boost the area of adversarial machine learning, which mainly fo-cus on evaluating model robustness through adversarial attacks and gain reliable model performance through adversarial defenses.Among this board topic, my research work focus on a practical perspective. Specifically, there are three subtopics: (1) Enhancing robustness performance for adversarial learning from feature perspective. (2) Standardized and Reliable evaluation for black box attacks under different settings. (3) Building user-friendly adversarial learning tools to help evaluate model robustness. In this research statement, we will mainly focus on these three topics and we will take this opportunity to share our contribution to the relate problems.
From Uni-relational to Multi-relational Graph Neural Networks
Authors: Juanhui Li
Venue: WSDM
Year: 2022

Graph Neural Networks (GNNs), which extend deep neural networks to graph-structured data, have attracted increasing attention. They have been proven to be powerful for numerous graph related tasks that cover a variety of research areas including natural language processing, information retrieval and knowledge graph completion (KGC). GNNs are primary designed for simple homogeneous and uni-relational graphs. Due to its great success in handling the graph data, considerable studies have been developed to extend GNNs to process complex multi-relational graphs such as the knowledge graph. My research first focuses on learning effective representation of uni-relational graph to facilitate some downstream applications such as graph classification and query understanding, and show the great capacity of GNNs to advance these tasks. Although the GNNs have demonstrated its significant effectiveness on the uni-relational graph in a large range of applications, we surprisingly found it may not be as crucial as previously believed in the knowledge graph completion task. It suggests careful attention to more suitable GNNs designs for KGC task.
Unsupervised Cross-Domain Adaptation for Response Selection Using Self-Supervised and Adversarial Training
Authors: Jia Li, Chongyang Tao, Huang Hu, Can Xu, Yining Chen, Daxin Jiang
Venue: WSDM
Year: 2022

Recently, many neural context-response matching models have been developed for retrieval-based dialogue systems. Although existing models achieve impressive performance through learning on a large amount of in-domain parallel dialogue data, they usually perform worse in another new domain. How to transfer a response retrieval model trained in high-resource domains to other low-resource domains is a crucial problem for scalable dialogue systems. To this end, we investigate the unsupervised cross-domain adaptation for response selection when the target domain has no parallel dialogue data. Specifically, we propose a two-stage method to adapt a response selection model to a new domain using self-supervised and adversarial training based on pre-trained language models (PLMs). To efficiently incorporate domain awareness and target-domain knowledge to PLMs, we first design a self-supervised post-training procedure, including domain discrimination (DD) task, target-domain masked language model (MLM) task and target-domain next sentence prediction (NSP) task. Based on this, we further conduct the adversarial fine-tuning to empower the model to match the proper response with extracted domain-shared features as much as possible. Experimental results show that our proposed method achieves consistent and significant improvements on several cross-domain response selection datasets.
a2RegInf: An Interactive System for Maximizing Influence within Arbitrary Number of Arbitrary Shaped Query Regions
Authors: Hui Li, Qiting Yang, Jiangtao Cui
Venue: WSDM
Year: 2022

Recently, aside with the prevalent usage of location-based social network, location-aware influence maximization (laim) problem has received plenty of attention in viral marketing. It aims to find a set of seed users such that information propagated from them can reach the largest number of users within particular geographical regions. However, existing solutions to laim can only work on single simple query region, e.g., a rectangle, instead of complex ones. Besides, there is no ready-to-use system for users to address laim visually. In this work, we present a pair of solutions towards location-aware influence maximization problem. Both can work on queries with arbitrary number of regions and arbitrary shapes. More importantly, we implement a web-based system, namely a2RegInf, which enables viral marketers to address laim visually, with native GPU support. To the best of our knowledge, we are the first to provide a ready-to-use system for answering the problem over web-based interface that supports arbitrary number of arbitrary shaped query regions.
Scalable Attribute Extraction at Instacart
Authors: Shih-Ting Lin
Venue: WSDM
Year: 2022

Structured attribute information extracted from natural text inputs has been extensively exploited in e-commerce to help improve the customer experience. For example, attributes can be extracted from the product catalog data such as product name & product descriptions; similarly, attributes can also be extracted from user queries to the search engine. Having these attribute information available can help greatly boost relevance of many different functionalities such as search, recommendation, and ads. However, with the huge space of product categories and extensive details in the product information, how to extract attribute information from text with high accuracy and high efficiency becomes an extremely challenging problem. In this talk, we will present the scalable machine learning-based attribute extraction pipeline we have built at Instacart for our online grocery business. We start our presentation with the unique challenges at Instacart on building our meta-catalog (catalog on top of catalogs from different retailers), and how we work with a diverse set of attribute naming conventions from multiple sources. After which we will talk about how we bootstrapped our attribute extraction work from scratch following a human-in-the-loop based solution, and trained our practical machine learning-based attribute extraction solution. We then present our achievement on unifying the attribute extraction on both user search queries and product textual information, and how we tackle the problem of mitigating the vocabulary gap between user search queries and product textual information in the catalog. Finally we present applications of our work in the real production environment and our learnings.
Trustworthy Machine Learning: Fairness and Robustness
Authors: Haochen Liu
Venue: WSDM
Year: 2022

In recent years, machine learning (ML) technologies have experienced swift developments and attracted extensive attention from both academia and industry. The applications of ML are extended to multiple domains, from computer vision, text processing, to recommendations, etc. However, recent studies have uncovered the untrustworthy side of ML applications. For example, ML algorithms could show human-like discrimination against certain individuals or groups, or make unreliable decisions in safety-critical scenarios, which implies the absence of fairness and robustness, respectively. Consequently, building trustworthy machine learning systems has become an urgent need. My research strives to help meet this demand. In particular, my research focuses on designing trustworthy ML models and spans across three main areas: (1) fairness in ML, where we aim to detect, eliminate bias and ensure fairness in various ML applications; (2) robustness in ML, where we seek to ensure the robustness of certain ML applications towards adversarial attacks; (3) specific applications of ML, where my research involves the development of ML-based natural language processing (NLP) models and recommendation systems.
A Practical Guide to Robust Multimodal Machine Learning and Its Application in Education
Authors: Zitao Liu
Venue: WSDM
Year: 2022

Recently we have seen a rapid rise in the amount of education data available through the digitization of education. This huge amount of education data usually exhibits in a mixture form of images, videos, speech, texts, etc. It is crucial to consider data from different modalities to build successful applications in AI in education (AIED). This talk targets AI researchers and practitioners who are interested in applying state-of-the-art multimodal machine learning techniques to tackle some of the hard-core AIED tasks. These include tasks such as automatic short answer grading, student assessment, class quality assurance, knowledge tracing, etc. In this talk, I will share some recent developments of successfully applying multimodal learning approaches in AIED, with a focus on those classroom multimodal data. Beyond introducing the recent advances of computer vision, speech, natural language processing in education respectively, I will discuss how to combine data from different modalities and build AI driven educational applications on top of these data. Participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use models, and how related models and techniques benefit real-world AIED applications.
Non-stationary Continuum-armed Bandits for Online Hyperparameter Optimization
Authors: Shiyin Lu, Yu-Hang Zhou, Jing-Cheng Shi, Wenya Zhu, Qingtao Yu, Qing-Guo Chen, Qing Da, Lijun Zhang
Venue: WSDM
Year: 2022

For years, machine learning has become the dominant approach to a variety of information retrieval tasks. The performance of machine learning algorithms heavily depends on their hyperparameters. It is hence critical to identity the optimal hyperparameter configuration when applying machine learning algorithms. Most of existing hyperparameter optimization methods assume a static relationship between hyperparameter configuration and algorithmic performance and are thus not suitable for many information retrieval applications with non-stationary environments such as e-commerce recommendation and online advertising. To address this limitation, we study online hyperparameter optimization, where the hyperparameter configuration is optimized on the fly. We formulate online hyperparameter optimization as a non-stationary continuum-armed bandits problem in which each arm corresponds to a hyperparameter configuration and the algorithmic performance is viewed as reward. For this problem, we develop principled methods with strong theoretical guarantees in terms of dynamic regret. The key idea is to adaptively discretize the continuous arm set and estimate the mean reward of each arm via weighted averaging. As a case application, we show how our methods can be applied to optimize the hyperparameter of vector-based candidate generation algorithm and empirically demonstrate the effectiveness and efficiency of our methods on public advertising dataset and online A/B testing. Furthermore, to the best of our knowledge, our methods are the first to achieve sub-linear dynamic regret bounds for continuum-armed bandits, which may be of independent interest.
The Multi-vehicle Ride-Sharing Problem
Authors: Kelin Luo, Chaitanya Agarwal, Syamantak Das, Xiangyu Guo
Venue: WSDM
Year: 2022

Ride-sharing is one of the most popular models of economical and eco-friendly transportation in modern smart cities, especially when riding hybrid and electric vehicles. Usually multiple passengers with similar itineraries are grouped together, which significantly reduces travel cost (or time), road congestion, and traffic emissions. In this paper, we study the ride-sharing problem where each vehicle is shared by exactly $łambda$ riders for any fixed $łambda>0$, and the goal is to minimize the total travel distance. The min-cost ride-sharing problem is intractable even in the case of exactly two riders sharing a vehicle \citeBeiZ18-carsharing, and hence we can only hope for an approximate solution. We propose a novel two-phase algorithm: a hierarchical grouping phase that partitions requests into disjoint groups of fixed size, followed by an assignment of request groups to individual vehicles and planning a feasible route for each vehicle. This is the first non-trivial approximation algorithm for the ride-sharing problem with vehicle capacity larger than two. We verify the efficacy of our algorithm on both synthetic and realworld datasets. Our experimental results show that, the ride-sharing scheme produced by our algorithm not only has small total travel distance compared to state-of-the-art baselines, but also enjoys a small makespan and total latency, which crucially relate to each single rider's traveling time. This suggests that our algorithm also enhances rider experience while being energy-efficient.