OpenRaiser/Intern-Atlas huggingface dataset:
JNET: Learning User Representations via Joint Network Embedding and Topic Embedding
Authors: Lin Gong, Lu Lin, Weihao Song, Hongning Wang
Venue: WSDM
Year: 2020

User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users' social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow.
Computer Vision for Fashion: From Individual Recommendations to World-wide Trends
Authors: Kristen Grauman
Venue: WSDM
Year: 2020

The fashion domain is a magnet for computer vision. New vision problems are emerging in step with the fashion industry's rapid evolution towards an online, social, and personalized business. Style models, trend forecasting, interactive search, and recommendation all require visual understanding with rich detail and subtlety. As a result, research in this area is poised to have great influence on how people shop, how the fashion industry analyzes its enterprise, and how we model the cultural trends revealed by what people wear. In this talk, I will present our work over the last few years developing computer vision methods for fashion. To begin, we explore how to discover styles from Web photos, learning how people assemble their outfits and the latent themes they share. Leveraging such styles, we show how to infer compatibility of new garments, optimize personalized mix-and-match capsule wardrobes, suggest minimal edits to make an outfit more fashionable, and recommend clothing that flatters diverse human body shapes. Next, turning to the world stage, we investigate fashion forecasting and influence. Given photos of fashion products, we learn to forecast what looks and styles will be popular in the future. We further boost those forecasts by modeling the spatio-temporal style influences between 44 major world cities. Throughout, by learning models from unlabeled Web photos, our approaches sidestep subjective manual annotations in favor of direct observations of what people choose to wear.
TSA: A Truthful Mechanism for Social Advertising
Authors: Tobias Grubenmann, Reynold C. K. Cheng, Laks V. S. Lakshmanan
Venue: WSDM
Year: 2020

Social advertising exploits the interconnectivity of users in social networks to spread advertisement and generate user engagements. A lot of research has focused on how to select the best subset of users in a social network to maximize the number of engagements or the generated revenue of the advertisement. However, there is a lack of studies that consider the advertiser's value-per-engagement, i.e., how much an advertiser is maximally willing to pay for each engagement. Prior work on social advertising is based on the classical framework of influence maximization. In this paper, we propose a model where advertisers compete in an auction mechanism for the influential users within a social network. The auction mechanism can dynamically determine payments for advertisers based on their reported values. The main problem is to find auctions which incentivize advertisers to truthfully reveal their values, and also respect each advertiser's budget constraint. To tackle this problem, we propose a new truthful auction mechanism called TSA. Compared with existing approaches on real and synthetic datasets, TSA performs significantly better in terms of generated revenue.
Learning Individual Causal Effects from Networked Observational Data
Authors: Ruocheng Guo, Jundong Li, Huan Liu
Venue: WSDM
Year: 2020

The convenient access to observational data enables us to learn causal effects without randomized experiments. This research direction draws increasing attention in research areas such as economics, healthcare, and education. For example, we can study how a medicine (the treatment) causally affects the health condition (the outcome) of a patient using existing electronic health records. To validate causal effects learned from observational data, we have to control confounding bias -- the influence of variables which causally influence both the treatment and the outcome. Existing work along this line overwhelmingly relies on the unconfoundedness assumption that there do not exist unobserved confounders. However, this assumption is untestable and can even be untenable. In fact, an important fact ignored by the majority of previous work is that observational data can come with network information that can be utilized to infer hidden confounders. For example, in an observational study of the individual-level treatment effect of a medicine, instead of randomized experiments, the medicine is often assigned to each individual based on a series of factors. Some of the factors (e.g., socioeconomic status) can be challenging to measure and therefore become hidden confounders. Fortunately, the socioeconomic status of an individual can be reflected by whom she is connected in social networks. With this fact in mind, we aim to exploit the network information to recognize patterns of hidden confounders which would further allow us to learn valid individual causal effects from observational data. In this work, we propose a novel causal inference framework, the network deconfounder, which learns representations to unravel patterns of hidden confounders from the network information. Empirically, we perform extensive experiments to validate the effectiveness of the network deconfounder on various datasets.
Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments
Authors: Somit Gupta, Xiaolin Shi, Pavel A. Dmitriev, Xin Fu, Avijit Mukherjee
Venue: WSDM
Year: 2020

A/B Testing is the gold standard to estimate the causal relationship between a change in a product and its impact on key outcome measures. It is widely used in the industry to test changes ranging from simple copy change or UI change to more complex changes like using machine learning models to personalize user experience. The key aspect of A/B testing is evaluation of experiment results. Designing the right set of metrics - correct outcome measures, data quality indicators, guardrails that prevent harm to business, and a comprehensive set of supporting metrics to understand the "why" behind the key movements is the #1 challenge practitioners face when trying to scale their experimentation program [11, 14]. On the technical side, improving sensitivity of experiment metrics is a hard problem and an active research area, with large practical implications as more and more small and medium size businesses are trying to adopt A/B testing and suffer from insufficient power. In this tutorial we will discuss challenges, best practices, and pitfalls in evaluating experiment results, focusing on both lessons learned and practical guidelines as well as open research questions. A version of this tutorial was also present at KDD 2019 [23]. It was attended by around 150 participants.
Entities with Quantities: Extraction, Search, and Ranking
Authors: Vinh Thinh Ho, Koninika Pal, Niko Kleer, Klaus Berberich, Gerhard Weikum
Venue: WSDM
Year: 2020

Quantities are more than numeric values. They represent measures for entities, expressed in numbers with associated units. Search queries often include quantities, such as athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. Processing such queries requires understanding the quantities, where capturing the surrounding context is an essential part of it. Although modern search engines or QA systems handle entity-centric queries well, they consider numbers and units as simple keywords, and therefore fail to understand the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.) As a result, they cannot generate the correct candidate answers. In this work, we demonstrate a prototype QA system, called Qsearch, that can handle advanced queries with quantity constraints using the common cues present in both query and the text sources.
WebShapes: Network Visualization with 3D Shapes
Authors: Shengmin Jin, Richard Wituszynski, Max Caiello-Gingold, Reza Zafarani
Venue: WSDM
Year: 2020

Network visualization has played a critical role in graph analysis, as it not only presents a big picture of a network but also helps reveal the structural information of a network. The most popular visual representation of networks is the node-link diagram. However, visualizing a large network with the node-link diagram can be challenging due to the difficulty in obtaining an optimal graph layout. To address this challenge, a recent advancement in network representation: network shape, allows one to compactly represent a network and its subgraphs with the distribution of their embeddings. Inspired by this research, we have designed a web platform WebShapes that enables researchers and practitioners to visualize their network data as customized 3D shapes (http://b.link/webshapes). Furthermore, we provide a case study on real-world networks to explore the sensitivity of network shapes to different graph sampling, embedding, and fitting methods, and we show examples of understanding networks through their network shapes.
Ultra Fine-Grained Image Semantic Embedding
Authors: Da-Cheng Juan, Chun-Ta Lu, Zhen Li, Futang Peng, Aleksei Timofeev, Yi-Ting Chen, Yaxi Gao, Tom Duerig, Andrew Tomkins, Sujith Ravi
Venue: WSDM
Year: 2020

"How to learn image embeddings that capture fine-grained semantics based on the instance of an image?" "Is it possible for such embeddings to further understand image semantics closer to humans' perception?" In this paper, we present, Graph-Regularized Image Semantic Embedding (Graph-RISE), a web-scale neural graph learning framework deployed at Google, which allows us to train image embeddings to discriminate an unprecedented O(40M) ultra-fine-grained semantic labels. The proposed Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including kNN search and triplet ranking: the accuracy is improved by approximately 2X on the ImageNet dataset and by more than 5X on the iNaturalist dataset. Qualitatively, image retrieval from one billion images based on the proposed Graph-RISE effectively captures semantics and, compared to the state-of-the-art, differentiates nuances at levels that are closer to human-perception.
Intelligible Machine Learning and Knowledge Discovery Boosted by Visual Means
Authors: Boris Kovalerchuk
Venue: WSDM
Year: 2020

Intelligible machine learning and knowledge discovery are important for modeling individual and social behavior, user activity, link prediction, community detection, crowd-generated data, and others. The role of the interpretable method in web search and mining activities is also very significant to enhance clustering, classification, data summarization, knowledge acquisition, opinion and sentiment mining, web traffic analysis, and web recommender systems. Deep learning success in accuracy of prediction and its failure in explanation of the produced models without special interpretation efforts motivated the surge of efforts to make Machine Learning (ML) models more intelligible and understandable. The prominence of visual methods in getting appealing explanations of ML models motivated the growth of deep visualization, and visual knowledge discovery. This tutorial covers the state-of-the-art research, development, and applications in the area of Intelligible Knowledge Discovery, and Machine Learning boosted by Visual Means.
Retrieving Top Weighted Triangles in Graphs
Authors: Raunak Kumar, Paul Liu, Moses Charikar, Austin R. Benson
Venue: WSDM
Year: 2020

Pattern counting in graphs is a fundamental primitive for many network analysis tasks, and there are several methods for scaling subgraph counting to large graphs. Many real-world networks have a notion of strength of connection between nodes, which is often modeled by a weighted graph, but existing scalable algorithms for pattern mining are designed for unweighted graphs. Here, we develop deterministic and random sampling algorithms that enable the fast discovery of the 3-cliques (triangles) of largest weight, as measured by the generalized mean of the triangle's edge weights. For example, one of our proposed algorithms can find the top-1000 weighted triangles of a weighted graph with billions of edges in thirty seconds on a commodity server, which is orders of magnitude faster than existing "fast" enumeration schemes. Our methods open the door towards scalable pattern mining in weighted graphs.
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
Authors: Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua
Venue: WSDM
Year: 2020

Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. We argue that three fundamental problems need to be solved: 1) what questions to ask regarding item attributes, 2) when to recommend items, and 3) how to adapt to the users' online feedback. To the best of our knowledge, there lacks a unified framework that addresses these problems. In this work, we fill this missing interaction framework gap by proposing a new CRS framework named Estimation"Action" Reflection, or EAR, which consists of three stages to better converse with users. (1) Estimation, which builds predictive models to estimate user preference on both items and item attributes; (2) Action, which learns a dialogue policy to determine whether to ask attributes or recommend items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage. We present two conversation scenarios on binary and enumerated questions, and conduct extensive experiments on two datasets from Yelp and LastFM, for each scenario, respectively. Our experiments demonstrate significant improvements over the state-of-the-art method CRM [32], corresponding to fewer conversation turns and a higher level of recommendation hits.
Think like a Human: Constructing Cognitive-oriented Retrieval Model for Web Search
Authors: Xiangsheng Li
Venue: WSDM
Year: 2020

Existing retrieval models have gained much success, however, we have to admit that the models work in a rather different manner than how humans make relevance judgments. To bridge the gap between practical user behavior and retrieval model, it is essential to construct cognitive-oriented retrieval model. The cognitive process in IR includes two important aspects: searching and reading. Searching is the user behaviors interacted with retrieval system such as query formulation and click while reading is the information seeking behavior in a specific landing page or document. We plan to better understand user cognitive behaviors in these two aspects by conducting a lab-based user study. More importantly, the heuristics drawn from cognitive behaviors are then incorporated into retrieval models.
Hybrid Utility Function for Unexpected Recommendations
Authors: Pan Li
Venue: WSDM
Year: 2020

Unexpectedness constitutes an important factor for recommender system to improve user satisfaction and avoid filter bubble issues. In this proposal, we propose to provide unexpected recommendations using the hybrid utility function as a mixture of estimated ratings, unexpectedness, relevance and annoyance. We plan to conduct extensive experiments to validate the superiority of the proposed method.
Interpretable Click-Through Rate Prediction through Hierarchical Attention
Authors: Zeyu Li, Wei Cheng, Yang Chen, Haifeng Chen, Wei Wang
Venue: WSDM
Year: 2020

Click-through rate (CTR) prediction is a critical task in online advertising and marketing. For this problem, existing approaches, with shallow or deep architectures, have three major drawbacks. First, they typically lack persuasive rationales to explain the outcomes of the models. Unexplainable predictions and recommendations may be difficult to validate and thus unreliable and untrustworthy. In many applications, inappropriate suggestions may even bring severe consequences. Second, existing approaches have poor efficiency in analyzing high-order feature interactions. Third, the polysemy of feature interactions in different semantic subspaces is largely ignored. In this paper, we propose InterHAt that employs a Transformer with multi-head self-attention for feature learning. On top of that, hierarchical attention layers are utilized for predicting CTR while simultaneously providing interpretable insights of the prediction results. InterHAt captures high-order feature interactions by an efficient attentional aggregation strategy with low computational complexity. Extensive experiments on four public real datasets and one synthetic dataset demonstrate the effectiveness and efficiency of InterHAt.
Automatic Speaker Recognition with Limited Data
Authors: Ruirui Li, Jyun-Yu Jiang, Jiahao Liu, Chu-Cheng Hsieh, Wei Wang
Venue: WSDM
Year: 2020

Automatic speaker recognition (ASR) is a stepping-stone technology towards semantic multimedia understanding and benefits versatile downstream applications. In recent years, neural network-based ASR methods have demonstrated remarkable power to achieve excellent recognition performance with sufficient training data. However, it is impractical to collect sufficient training data for every user, especially for fresh users. Therefore, a large portion of users usually has a very limited number of training instances. As a consequence, the lack of training data prevents ASR systems from accurately learning users acoustic biometrics, jeopardizes the downstream applications, and eventually impairs user experience. In this work, we propose an adversarial few-shot learning-based speaker identification framework (AFEASI) to develop robust speaker identification models with only a limited number of training instances. We first employ metric learning-based few-shot learning to learn speaker acoustic representations, where the limited instances are comprehensively utilized to improve the identification performance. In addition, adversarial learning is applied to further enhance the generalization and robustness for speaker identification with adversarial examples. Experiments conducted on a publicly available large-scale dataset demonstrate that \model significantly outperforms eleven baseline methods. An in-depth analysis further indicates both effectiveness and robustness of the proposed method.
Incremental Filter Pruning via Random Walk for Accelerating Deep Convolutional Neural Networks
Authors: Qinghua Li, Cuiping Li, Hong Chen
Venue: WSDM
Year: 2020

Accelerating Deep Convolutional Neural Networks (CNNs) has recently received ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. Previous works utilized "smaller-norm-less-important" criterion to prune filters with smaller ࡁp-norm values by pruning and retraining alternately. This trends to narrow the model capacity for the following reasons: (1) Violent pruning. Previous works adopt a violent strategy in which all filters are simultaneously pruned, which leaving the room to retain model accuracy limited. (2) Filter degradation. Previous works simply set the pruned filter to 0 and retrained it alterately, which easily led to the loss of learning ability of filters. To solve this problem, we propose a novel filter pruning method, namely Incremental Filter Pruning via Random Walk (IFPRW). IFPRW solves the problem of violent pruning by incremental method and Filter degradation by means of random walk. When applied to two image classification benchmarks, the usefulness and strength of IFPRW is validated. Notably, on CIFAR-10, IFPRW reduces more than 46% FLOPs on ResNet-110 with even 0.28% relative accuracy improvement. Moreover, on ILSVRC-2012, IFPRW reduces more than 54% FLOPs on ResNet-101 with only 0.7% top-5 accurcacy drop. which proving that IFPRW outperforms the state-of-the-art filter pruning methods.
Adversarial Learning to Compare: Self-Attentive Prospective Customer Recommendation in Location based Social Networks
Authors: Ruirui Li, Xian Wu, Wei Wang
Venue: WSDM
Year: 2020

Recommendation systems tend to suffer severely from the sparse training data. A large portion of users and items usually have a very limited number of training instances. The data sparsity issue prevents us from accurately understanding users' preferences and items' characteristics and jeopardize the recommendation performance eventually. In addition, models, trained with sparse data, lack abundant training supports and tend to be vulnerable to adversarial perturbations, which implies possibly large errors in generalization. In this work, we investigate the recommendation task in the context of prospective customer recommendation in location based social networks. To comprehensively utilize the training data, we explicitly learn to compare users' historical check-in businesses utilizing self-attention mechanisms. To enhance the robustness of a recommender system and improve its generalization performance, we perform adversarial training. Adversarial perturbations are dynamically constructed during training and models are trained to be tolerant of such nuisance perturbations. In a nutshell, we introduce a Self-Attentive prospective Customer RecommendAtion framework, SACRA, which learns to recommend by making comparisons among users' historical check-ins with adversarial training. To evaluate the proposed model, we conduct a series of experiments to extensively compare with 12 existing methods using two real-world datasets. The results demonstrate that SACRA significantly outperforms all baselines.