OpenRaiser/Intern-Atlas huggingface dataset:
S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks
Authors: Minjin Choi, Jinhong Kim, Joonseok Lee, Hyunjung Shim, Jongwuk Lee
Venue: WSDM
Year: 2022

Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user. Most existing SR models focus only on modeling intra-session characteristics but pay less attention to inter-session relationships of items, which has the potential to improve accuracy. Another critical aspect of recommender systems is computational efficiency and scalability, considering practical feasibility in commercial applications. To account for both accuracy and scalability, we propose a novel session-based recommendation with a random walk, namely S-Walk. Precisely, S-Walk effectively captures intra- and inter-session correlations by handling high-order relationships among items using random walks with restart (RWR). By adopting linear models with closed-form solutions for transition and teleportation matrices that constitute RWR, S-Walk is highly efficient and scalable. Extensive experiments demonstrate that S-Walk achieves comparable or state-of-the-art performance in various metrics on four benchmark datasets. Moreover, the model learned by S-Walk can be highly compressed without sacrificing accuracy, conducting two or more orders of magnitude faster inference than existing DNN-based models, making it suitable for large-scale commercial systems.
ANTHEM: Attentive Hyperbolic Entity Model for Product Search
Authors: Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy
Venue: WSDM
Year: 2022

Product search is a fundamentally challenging problem due to the large-size of product catalogues and the complexity of extracting semantic information from products. In addition to this, the black-box nature of most search systems also hamper a smooth customer experience. Current approaches in this area utilize lexical and semantic product information to match user queries against products. However, these models lack (i) a hierarchical query representation, (ii) a mechanism to detect and capture inter-entity relationships within a query, and (iii) a query composition method specific to e-commerce domain. To address these challenges, in this paper, we propose an AtteNTive Hyperbolic Entity Model (ANTHEM), a novel attention-based product search framework that models query entities as two-vector hyperboloids, learns inter-entity intersections and utilizes attention to unionize individual entities and inter-entity intersections to predict product matches from the search space. ANTHEM utilizes the first and second vector of hyperboloids to determine the query's semantic position and to tune its surrounding search volume, respectively. The attention networks capture the significance of intra-entity and inter-entity intersections to the final query space. Additionally, we provide a mechanism to comprehend ANTHEM and understand the significance of query entities towards the final resultant products. We evaluate the performance of our model on real data collected from popular e-commerce sites. Our experimental study on the offline data demonstrates compelling evidence of ANTHEM's superior performance over state-of-the-art product search methods with an improvement of more than 10% on various metrics. We also demonstrate the quality of ANTHEM's query encoder using a query matching task.
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
Authors: Enyan Dai, Wei Jin, Hui Liu, Suhang Wang
Venue: WSDM
Year: 2022

Graph Neural Networks (GNNs) have shown their great ability in modeling graph structured data. However, real-world graphs usually contain structure noises and have limited labeled nodes. The performance of GNNs would drop significantly when trained on such graphs, which hinders the adoption of GNNs on many applications. Thus, it is important to develop noise-resistant GNNs with limited labeled nodes. However, the work on this is rather limited. Therefore, we study a novel problem of developing robust GNNs on noisy graphs with limited labeled nodes. Our analysis shows that both the noisy edges and limited labeled nodes could harm the message-passing mechanism of GNNs. To mitigate these issues, we propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph, which can down-weight or eliminate noisy edges and facilitate message passing of GNNs to alleviate the issue of limited labeled nodes. The generated edges are further used to regularize the predictions of unlabeled nodes with label smoothness to better train GNNs. Experimental results on real-world datasets demonstrate the robustness of the proposed framework on noisy graphs with limited labeled nodes.
Predicting Human Mobility via Graph Convolutional Dual-attentive Networks
Authors: Weizhen Dang, Haibo Wang, Shirui Pan, Pei Zhang, Chuan Zhou, Xin Chen, Jilong Wang
Venue: WSDM
Year: 2022

Human mobility prediction is of great importance for various applications such as smart transportation and personalized recommender systems. Although many traditional pattern-based methods and deep models ($e.g.,$ recurrent neural networks) based methods have been developed for this task, they essentially do not well cope with the sparsity and inaccuracy of trajectory data and the complicated high-order nature of the sequential dependency, which are typical challenges in mobility prediction. To solve the problems, this paper proposes a novel framework named G raph C onvolutional D ual-a ttentive N etworks (GCDAN), which consists of two modules: spatio-temporal embedding and trajectory encoder-decoder. The first module employs a bidirectional diffusion graph convolution to preserve the spatial dependency in the location embedding. The second module employs a dual-attentive mechanism based on a Sequence to Sequence architecture to effectively extract the long-range sequential dependency within a trajectory and the correlation between different trajectories for predictions. Extensive experiments on three real-world datasets show that GCDAN achieves significant performance gain compared with state-of-the-art baselines.
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Query Performance Prediction
Authors: Suchana Datta, Debasis Ganguly, Derek Greene, Mandar Mitra
Venue: WSDM
Year: 2022

Motivated by the recent success of end-to-end deep neural models for ranking tasks, we present here a supervised end-to-end neural approach for query performance prediction (QPP). In contrast to unsupervised approaches that rely on various statistics of document score distributions, our approach is entirely data-driven. Further, in contrast to weakly supervised approaches, our method also does not rely on the outputs from different QPP estimators. In particular, our model leverages information from the semantic interactions between the terms of a query and those in the top-documents retrieved with it. The architecture of the model comprises multiple layers of 2D convolution filters followed by a feed-forward layer of parameters. Experiments on standard test collections demonstrate that our proposed supervised approach outperforms other state-of-the-art supervised and unsupervised approaches.
Improving Personalized Search with Dual-Feedback Network
Authors: Chenlong Deng, Yujia Zhou, Zhicheng Dou
Venue: WSDM
Year: 2022

Personalized search improves the quality of search results by modeling historical user behavior. In recent years, many methods based on deep learning have greatly improved the performance of personalized search. However, most of the existing methods only focus on modeling positive user behavior signals, which leads to incomplete user interest modeling. At the same time, the user's search behavior hides much explicit or implicit feedback information. For example, clicking and staying for a certain period represents implicit positive feedback, and skipping behavior represents implicit negative feedback. Intuitively, this information can be utilized to construct a more complete and accurate user profile. In this paper, we propose a dual-feedback modeling framework, which integrates multi-granular user feedback information to model the user's current search intention. Specifically, we propose a feedback extraction network to refine the dual-feedback representation in multiple stages. For enhancing the user's real-time search quality, we design an additional dual-feedback feature gating module to capture the user's real-time feedback in the current session. We conducted a large number of experiments on two real-world datasets, and the experimental results show that our method can effectively improve the performance of personalized search.
Aligning the Research and Practice of Building Search Applications: Elasticsearch and Pyserini
Authors: Josh Devins, Julie Tibshirani, Jimmy Lin
Venue: WSDM
Year: 2022

We demonstrate, via competitive bag-of-words first-stage retrieval baselines for the MS MARCO document ranking task, seamless replicability and interoperability between Elasticsearch and the Pyserini IR toolkit, which are both built on the open-source Lucene search library. This integration highlights the benefits of recent efforts to promote the use of Lucene in information retrieval research to better align the research and practice of building search applications. Closer alignment between academia and industry is mutually beneficial: Academic researchers gain a smoother path to real-world impact because their contributions can be more easily deployed in production applications. Industry practitioners gain an easy way to benchmark their innovations in a rigorous and vendor-neutral manner by exploiting evaluation resources and infrastructure built by the academic community. This two-way exchange between academia and industry allows both parties to "have their cakes and eat them too".
Graph Minimally-supervised Learning
Authors: Kaize Ding, Jundong Li, Nitesh V. Chawla, Huan Liu
Venue: WSDM
Year: 2022

Graphs are widely used for abstracting complex systems of interacting objects, such as social networks, knowledge graphs, and traffic networks, as well as for modeling molecules, manifolds, and source code. To model such graph-structured data, graph learning, in particular deep graph learning with graph neural networks, has drawn much attention in both academic and industrial communities lately. Prevailing graph learning methods usually rely on learning from "big'' data, requiring a large amount of labeled data for model training. However, it is common that graphs are associated with "small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph learning with minimal human supervision for the low-resource settings where limited or even no labeled data is available. In this tutorial, we will focus on the state-of-the-art techniques of Graph Minimally-supervised Learning, in particular a series of weakly-supervised learning, few-shot learning, and self-supervised learning methods on graph-structured data as well as their real-world applications. The objectives of this tutorial are to: (1) formally categorize the problems in graph minimally-supervised learning and discuss the challenges under different learning scenarios; (2) comprehensively review the existing and recent advances of graph minimally-supervised learning; and (3) elucidate open questions and future research directions. This tutorial introduces major topics within minimally-supervised learning and offers a guide to a new frontier of graph learning. We believe this tutorial is beneficial to researchers and practitioners, allowing them to collaborate on graph learning.
Robust Graph Learning for Misbehavior Detection
Authors: Yingtong Dou
Venue: WSDM
Year: 2022

Recent years have witnessed the thriving of online services like social media, e-commerce, and e-finance. Those services facilitate our daily lives while breeding malicious actors like fraudsters and spammers to promote misinformation, gain monetary rewards, or reap end users' privacy. Graph-based machine learning models have been playing a critical and irreplaceable role in modeling and detecting online misbehavior. With the observation that misbehaviors are different from massive regular behaviors, the graph models can leverage the relationship between data entities from a holistic view and reveal suspicious behaviors as anomalous nodes/edges/subgraphs on the graph. In this proposal, we investigate the graph-based misbehavior detection models from an adversarial perspective, considering the adversarial nature of malicious actors and real-world factors that impair graph models' robustness. We first introduce two published works enhancing the robustness of several graph-based misbehavior detectors using reinforcement learning. Then, we propose to explore: 1) the robustness of graph neural networks for misinformation detection on social media; and 2) the general robustness of graph neural networks towards unknown perturbations.
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective
Authors: Lun Du, Xu Chen, Fei Gao, Qiang Fu, Kunqing Xie, Shi Han, Dongmei Zhang
Venue: WSDM
Year: 2022

Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks. However, both the noisy information behind the real-world networks and the overfitting problem negatively impact the quality of embedding vectors. To tackle these problems, researchers utilize Adversarial Perturbations on Parameters (APP) and achieve state-of-the-art performance. Unlike the mainstream methods introducing perturbations on the network structure or the data feature, Adversarial Training for Network Embedding (AdvTNE) adopts APP to directly perturb the model parameters, thus providing a new chance to understand the mechanism behind it. In this paper, we explain APP theoretically from an optimization perspective. Considering the Power-law property of networks and the optimization objective, we analyze the reason for its remarkable results on network embedding. Based on the above analysis and the Sigmoid saturation region problem, we propose a new Sine-base activation to enhance the performance of AdvTNE. We conduct extensive experiments on four real networks to validate the effectiveness of our method in node classification and link prediction. The results demonstrate that our method is competitive with state-of-the-art methods.
Semi-supervised Stance Detection of Tweets Via Distant Network Supervision
Authors: Subhabrata Dutta, Samiya Caur, Soumen Chakrabarti, Tanmoy Chakraborty
Venue: WSDM
Year: 2022

Detecting and labeling stance in social media text is strongly motivated by hate speech detection, poll prediction, engagement forecasting, and concerted propaganda detection. Today's best neural stance detectors need large volumes of training data, which is difficult to curate given the fast-changing landscape of social media text and issues on which users opine. Homophily properties over the social network provide strong signal of coarse-grained user-level stance. But semi-supervised approaches for tweet-level stance detection fail to properly leverage homophily. In light of this, We present SANDS, a new semi-supervised stance detector. SANDS starts from very few labeled tweets. It builds multiple deep feature views of tweets. It also uses a distant supervision signal from the social network to provide a surrogate loss signal to the component learners. We prepare two new tweet datasets comprising over 236,000 politically tinted tweets from two demographics (US and India) posted by over 87,000 users, their follower-followee graph, and over 8,000 tweets annotated by linguists. SANDS achieves a macro-F1 score of 0.55 (0.49) on US (India)-based datasets, outperforming 17 baselines (including variants of SANDS) substantially, particularly for minority stance labels and noisy text. Numerous ablation experiments on SANDS disentangle the dynamics of textual and network-propagated stance signals.
External Evaluation of Ranking Models under Extreme Position-Bias
Authors: Yaron Fairstein, Elad Haramaty, Arnon Lazerson, Liane Lewin-Eytan
Venue: WSDM
Year: 2022

Implicit feedback from users behavior is a natural and scalable source for training and evaluating ranking models in human-interactive systems. However, inherent biases such as the position bias are key obstacles to its effective usage. This is further accentuated in cases of extreme bias, where behavioral feedback can be collected exclusively on the top ranked result. In fact, in such cases, state-of-art debiasing methods cannot be applied. A prominent use case of extreme position bias is the voice shopping medium, where only a small amount of information can be presented to the user during a single interaction, resulting in user behavioral signals that are almost exclusively limited to the top offer. There is no way to know how the user would have reacted to a different offer than the top one he was actually exposed to. Thus, any new ranker we wish to evaluate with respect to a behavioral metric, requires online experimentation. We propose a novel approach, based on anexternal estimator model, for accurately predicting offline the performance of a new ranker. The accuracy of our solution is proven theoretically, as well as demonstrated by a line of experiments. In these experiments, we focus on the use case of purchase prediction, and show that our estimator can accurately predict offline the purchase rate of different rankers over a segment of voice shopping traffic. Our prediction is validated online, as being compared to the actual performance obtained by each ranker when being exposed to users.
Building Multi-turn Query Interpreters for E-commercial Chatbots with Sparse-to-dense Attentive Modeling
Authors: Yan Fan, Chengyu Wang, Peng He, Yunhua Hu
Venue: WSDM
Year: 2022

Predicting query intents is crucial for understanding user demands in chatbots. In real-world applications, accurate query intent classification can be highly challenging as human-machine interactions are often conducted in multiple turns, which requires the models to capture related information from the entire contexts. In addition, query intents tend to be fine-grained (up to hundreds of classes), containing lots of casual chats without clear intents. Hence, it is difficult for standard transformer-based models to capture complicated language characteristics of dialogues to support these applications. In this demo, we present AliMeTerp, a multi-turn query interpretation system, which can be seamlessly integrated into e-commercial chatbots in order to generate appropriate responses. Specifically, in AliMeTerp, we introduce SAM-BERT, a pre-trained language model for fine-grained query intent understanding, based on Sparse-to-dense Attentive Modeling. For model pre-training, a stack of Sparse-to-dense Attentive Encoders are employed to model the complicated dialogue structures from different levels. We further design Hierarchical Multi-grained Classification tasks for model fine-tuning. Experiments show SAM-BERT consistently outperforms strong baselines over multiple multi-turn chatbot datasets. We further show how AliMeTerp is deployed in real-world e-commercial chatbots to support real-time customer service.
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
Authors: Zhifang Fan, Dan Ou, Yulong Gu, Bairan Fu, Xiang Li, Wentian Bao, Xin-Yu Dai, Xiaoyi Zeng, Tao Zhuang, Qingwen Liu
Venue: WSDM
Year: 2022

Modeling user's historical feedback is essential for Click-Through Rate Prediction in personalized search and recommendation. Existing methods usually only model users' positive feedback information such as click sequences which neglects the context information of the feedback. In this paper, we propose a new perspective for context-aware users' behavior modeling by including the whole page-wisely exposed products and the corresponding feedback as contextualized page-wise feedback sequence. The intra-page context information and inter-page interest evolution can be captured to learn more specific user preference. We design a novel neural ranking model RACP(Recurrent Attention over Contextualized Page sequence), which utilizes page-context aware attention to model the intra-page context. A recurrent attention process is used to model the cross-page interest convergence evolution as denoising the interest in the previous pages. Experiments on public and real-world industrial datasets verify our model's effectiveness.
Graph Neural Networks for Recommender System
Authors: Chen Gao, Xiang Wang, Xiangnan He, Yong Li
Venue: WSDM
Year: 2022

Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse and various in the real world, it is quite challenging to design proper GNN methods for specific problems. In this tutorial, we focus on the critical challenges of GNN-based recommendation and the potential solutions. Specifically, we start from an extensive background of recommender systems and graph neural networks. Then we fully discuss why GNNs are required in recommender systems and the four parts of challenges, including graph construction, network design, optimization, and computation efficiency. Then, we discuss how to address these challenges by elaborating on the recent advances of GNN-based recommendation models, with a systematic taxonomy from four critical perspectives: stages, scenarios, objectives, and applications. Last, we finalize this tutorial with conclusions and discuss important future directions.
HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling
Authors: Shen Gao, Yuchi Zhang, Yongliang Wang, Yang Dong, Xiuying Chen, Dongyan Zhao, Rui Yan
Venue: WSDM
Year: 2022

Community Question Answering (CQA) is a well-defined task that can be used in many scenarios, such as E-Commerce and online user community for special interests. In these communities, users can post articles, give comment, raise a question and answer it. These data form the heterogeneous information sources where each information source have their own special structure and context (comments attached to an article or related question with answers). Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question. However, various types of information sources in the community are not fully explored by these CQA methods and these multiple information sources (MIS) can provide more related knowledge to user's questions. Thus, we propose a question-aware heterogeneous graph transformer to incorporate the MIS in the user community to automatically generate the answer. To evaluate our proposed method, we conduct the experiments on two datasets: $\textMSM ^\textplus $ the modified version of benchmark dataset MS-MARCO and the AntQA dataset which is the first large-scale CQA dataset with four types of MIS. Extensive experiments on two datasets show that our model outperforms all the baselines in terms of all the metrics.