OpenRaiser/Intern-Atlas huggingface dataset:
Influence Maximization with Spontaneous User Adoption
Authors: Lichao Sun, Albert C. Chen, Philip S. Yu, Wei Chen
Venue: WSDM
Year: 2020

We incorporate the realistic scenario of spontaneous user adoption into influence propagation (also refer to as self-activation) and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as seeds, and influence propagates from both selected seeds and self activated nodes. Self activation occurs in many real world situations; for example, people naturally share product recommendations with their friends, even without marketing intervention. Under the SAIC model, we study three influence maximization problems: (a) boosted influence maximization (BIM) aims to maximize the total influence spread from both self-activated nodes and k selected seeds; (b) preemptive influence maximization (PIM) aims to find k nodes that, if self-activated, can reach the most number of nodes before other self-activated nodes; and (c) boosted preemptive influence maximization (BPIM) aims to select k seed that are guaranteed to be activated and can reach the most number of nodes before other self-activated nodes. We propose scalable algorithms for all three problems and prove that they achieve $1-1/e-\varepsilon$ approximation for BIM and BPIM and $1-\varepsilon$ for PIM, for any $\varepsilon > 0$. Through extensive tests on real-world graphs, we demonstrate that our algorithms outperform the baseline algorithms significantly for the PIM problem in solution quality, and also outperform the baselines for BIM and BPIM when self-activation behaviors are nonuniform across nodes.
LARA: Attribute-to-feature Adversarial Learning for New-item Recommendation
Authors: Changfeng Sun, Han Liu, Meng Liu, Zhaochun Ren, Tian Gan, Liqiang Nie
Venue: WSDM
Year: 2020

Recommending new items in real-world e-commerce portals is a challenging problem as the cold start phenomenon, i.e., lacks of user-item interactions. To address this problem, we propose a novel recommendation model, i.e., adversarial neural network with multiple generators, to generate users from multiple perspectives of items' attributes. Namely, the generated users are represented by attribute-level features. As both users and items are attribute-level representations, we can implicitly obtain user-item attribute-level interaction information. In light of this, the new item can be recommended to users based on attribute-level similarity. Extensive experimental results on two item cold-start scenarios, movie and goods recommendation, verify the effectiveness of our proposed model as compared to state-of-the-art baselines.
Fast Item Ranking under Neural Network based Measures
Authors: Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li
Venue: WSDM
Year: 2020

Recently, plenty of neural network based recommendation models have demonstrated their strength in modeling complicated relationships between heterogeneous objects (i.e., users and items). However, the applications of these fine trained recommendation models are limited to the off-line manner or the re-ranking procedure (on a pre-filtered small subset of items), due to their time-consuming computations. Fast item ranking under learned neural network based ranking measures is largely still an open question. In this paper, we formulate ranking under neural network based measures as a generic ranking task, Optimal Binary Function Search (OBFS), which does not make strong assumptions for the ranking measures. We first analyze limitations of existing fast ranking methods (e.g., ANN search) and explain why they are not applicable for OBFS. Further, we propose a flexible graph-based solution for it, Binary Function Search on Graph (BFSG). It can achieve approximate optimal efficiently, with accessible conditions. Experiments demonstrate effectiveness and efficiency of the proposed method, in fast item ranking under typical neural network based measures.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
Authors: Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang
Venue: WSDM
Year: 2020

Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable modifications on graph topology or nodal features can significantly reduce the performances of GNNs. It is very challenging to design robust graph neural networks against poisoning attack and several efforts have been taken. Existing work aims at reducing the negative impact from adversarial edges only with the poisoned graph, which is sub-optimal since they fail to discriminate adversarial edges from normal ones. On the other hand, clean graphs from similar domains as the target poisoned graph are usually available in the real world. By perturbing these clean graphs, we create supervised knowledge to train the ability to detect adversarial edges so that the robustness of GNNs is elevated. However, such potential for clean graphs is neglected by existing work. To this end, we investigate a novel problem of improving the robustness of GNNs against poisoning attacks by exploring clean graphs. Specifically, we propose PA-GNN, which relies on a penalized aggregation mechanism that directly restrict the negative impact of adversarial edges by assigning them lower attention coefficients. To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph. Experimental results on four real-world datasets demonstrate the robustness of PA-GNN against poisoning attacks on graphs.
Learning and Reasoning on Graph for Recommendation
Authors: Xiang Wang, Xiangnan He, Tat-Seng Chua
Venue: WSDM
Year: 2020

Recommendation methods construct predictive models to estimate the likelihood of a user-item interaction. Previous models largely follow a general supervised learning paradigm - treating each interaction as a separate data instance and building a supervised learning model upon the information isolated island. Such paradigm, however, overlook relations among data instances, hence easily resulting in suboptimal performance especially for sparse scenarios. Moreover, due to the black-box nature, most models hardly exhibit the reasons behind a prediction, making the recommendation process opaque to understand. In this tutorial, we revisit the recommendation problem from the perspective of graph learning and reasoning. Common data sources for recommendation can be organized into graphs, such as bipartite user-item interaction graphs, social networks, item knowledge graphs (heterogeneous graphs), among others. Such a graph-based organization connects the isolated data instances and exhibits relationships among instances as high-order connectivities, thereby encoding meaningful patterns for collaborative filtering, content-based filtering, social influence modeling, and knowledgeaware reasoning. Inspired by this, prior studies have incorporated graph analysis (e.g., random walk) and graph learning (e.g., network embedding) into recommender models and achieved great success. Together with the recent success of graph neural networks (GNNs), graph-based models have exhibited the potential to be the technologies for next-generation recommender systems. This tutorial provides a review on graph-based learning methods for recommendation, with special focus on recent developments of GNNs. By introducing this emerging and promising topic in this tutorial, we expect the audience to get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies.
Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce
Authors: Jianling Wang, Raphael Louca, Diane Hu, Caitlin Cellier, James Caverlee, Liangjie Hong
Venue: WSDM
Year: 2020

Currently, most sequence-based recommendation models aim to predict a user's next actions (e.g. next purchase) based on their past actions. These models either capture users' intrinsic preference (e.g. a comedy lover, or a fan of fantasy) from their long-term behavior patterns or infer their current needs by emphasizing recent actions. However, in e-commerce, intrinsic user behavior may be shifted by occasions such as birthdays, anniversaries, or gifting celebrations (Valentine's Day or Mother's Day), leading to purchases that deviate from long-term preferences and are not related to recent actions. In this work, we propose a novel next-item recommendation system which models a user's default, intrinsic preference, as well as two different kinds of occasion-based signals that may cause users to deviate from their normal behavior. More specifically, this model is novel in that it: (1) captures a personal occasion signal using an attention layer that models reoccurring occasions specific to that user (e.g. a birthday); (2) captures a global occasion signal using an attention layer that models seasonal or reoccurring occasions for many users (e.g. Christmas); (3) balances the user's intrinsic preferences with the personal and global occasion signals for different users at different timestamps with a gating layer. We explore two real-world e-commerce datasets (Amazon and Etsy) and show that the proposed model outperforms state-of-the-art models by 7.62% and 6.06% in predicting users' next purchase.
Deep Learning for Anomaly Detection
Authors: Ruoying Wang, Kexin Nie, Tie Wang, Yang Yang, Bo Long
Venue: WSDM
Year: 2020

Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires the researchers/developers to learn the complex structure from noisy data, identify the dynamic anomaly patterns and detect anomalies while lacking sufficient labels. Recent advancement in deep learning techniques has made it possible to largely improve anomaly detection performance compared to the classical approaches. This tutorial will help the audience gain a comprehensive understanding of deep learning-based anomaly detection techniques in various application domains. First, it introduces what is the anomaly detection problem, the approaches taken before the deep model era and the challenges it faced. Then it surveys the state-of-the-art deep learning models extensively and discusses the techniques used to overcome the limitations from traditional algorithms. Second to last, it studies deep model anomaly detection techniques in real world examples from LinkedIn production systems. The tutorial concludes with a discussion of future trends.
VISION-KG: Topic-centric Visualization System for Summarizing Knowledge Graph
Authors: Jiaqi Wei, Shuo Han, Lei Zou
Venue: WSDM
Year: 2020

Large scale knowledge graph (KG) has attracted wide attentions in both academia and industry recently. However, due to the complexity of SPARQL syntax and massive volume of real KG, it remains difficult for ordinary users to access KG. In this demo, we present VISION-KG, a topic-centric visualization system to help users navigate KG easily via entity summarization and entity clustering. Given a query entity v0, VISION-KG summarizes the induced subgraph of v0's neighbor nodes via our proposed facts ranking method that measures importance, relatedness and diversity. Moreover, to achieve conciseness, we split the summarized graph into several topic-centric summarized subgraph according to semantic and structural similarities among entities. We will demonstrate how VISION-KG provides a user-friendly visualization interface for navigating KG.
Metrics, User Models, and Satisfaction
Authors: Alfan Farizki Wicaksono, Alistair Moffat
Venue: WSDM
Year: 2020

User satisfaction is an important factor when evaluating search systems, and hence a good metric should give rise to scores that have a strong positive correlation with user satisfaction ratings. A metric should also correspond to a plausible user model, and hence provide a tangible manifestation of how users interact with search rankings. Recent work has focused on metrics whose user models accurately portray the behavior of search engine users. Here we investigate whether those same metrics then also correlate with user satisfaction. We carry out experiments using various classes of metrics, and confirm through the lens of the C/W/L framework that the metrics with user models that reflect typical behavior also tend to be the metrics that correlate well with user satisfaction ratings.
PERQ: Predicting, Explaining, and Rectifying Failed Questions in KB-QA Systems
Authors: Zhiyong Wu, Ben Kao, Tien-Hsuan Wu, Pengcheng Yin, Qun Liu
Venue: WSDM
Year: 2020

A knowledge-based question-answering (KB-QA) system is one that answers natural-language questions by accessing information stored in a knowledge base (KB). Existing KB-QA systems generally register an accuracy of 70-80% for simple questions and less for more complex ones. We observe that certain questions are intrinsically difficult to answer correctly with existing systems. We propose the PERQ framework to address this issue. Given a question q, we perform three steps to boost answer accuracy: (1) (Prediction) We predict if q can be answered correctly by a KB-QA system S. (2) (Explanation) If S is predicted to fail q, we analyze them to determine the most likely reasons of the failure. (3) (Rectification) We use the prediction and explanation results to rectify the answer. We put forward tools to achieve the three steps and analyze their effectiveness. Our experiments show that the PERQ framework can significantly improve KB-QA systems' accuracies over simple questions.
Knowledge-aware Complementary Product Representation Learning
Authors: Da Xu, Chuanwei Ruan, Jason H. D. Cho, Evren Körpeoglu, Sushant Kumar, Kannan Achan
Venue: WSDM
Year: 2020

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers.
Sampling Subgraphs with Guaranteed Treewidth for Accurate and Efficient Graphical Inference
Authors: Jaemin Yoo, U Kang, Mauro Scanagatta, Giorgio Corani, Marco Zaffalon
Venue: WSDM
Year: 2020

How can we run graphical inference on large graphs efficiently and accurately? Many real-world networks are modeled as graphical models, and graphical inference is fundamental to understand the properties of those networks. In this work, we propose a novel approach for fast and accurate inference, which first samples a small subgraph and then runs inference over the subgraph instead of the given graph. This is done by the bounded treewidth (BTW) sampling, our novel algorithm that generates a subgraph with guaranteed bounded treewidth while retaining as many edges as possible. We first analyze the properties of BTW theoretically. Then, we evaluate our approach on node classification and compare it with the baseline which is to run loopy belief propagation (LBP) on the original graph. Our approach can be coupled with various inference algorithms: it shows higher accuracy up to 13.7% with the junction tree algorithm, and allows faster inference up to 23.8 times with LBP. We further compare BTW with previous graph sampling algorithms and show that it gives the best accuracy.
Learning from Heterogeneous Networks: Methods and Applications
Authors: Chuxu Zhang
Venue: WSDM
Year: 2020

Complex systems in different disciplines are usually modeled as heterogeneous networks. Different from homogeneous networks or attributed networks, heterogeneous networks are associated with complexity in heterogeneous structure or heterogeneous content or both. The abundant information in heterogeneous networks provide opportunities yet pose challenges for researchers and practitioners to develop customized machine learning solutions for solving different problems in complex systems. We are motivated to do significant work for learning from heterogeneous networks. In this paper, we first introduce the motivation and background of this research. Later, we present our current work which include a series of proposed methods and applications. These methods will be introduced in the perspectives of personalization in web-based systems and heterogeneous network embedding. In the end, we raise several research directions as future agenda.
Nearly Linear Time Algorithm for Mean Hitting Times of Random Walks on a Graph
Authors: Zuobai Zhang, Wanyue Xu, Zhongzhi Zhang
Venue: WSDM
Year: 2020

For random walks on a graph, the mean hitting time $H_j$ from a vertex i chosen from the stationary distribution to the target vertex j can be used as a measure of importance for vertex j, while the Kemeny constant K is the mean hitting time from a vertex i to a vertex j selected randomly according to the stationary distribution. Both quantities have found a large variety of applications in different areas. However, their high computational complexity limits their applications, especially for large networks with millions of vertices. In this paper, we first establish a connection between the two quantities, representing K in terms of $H_j$ for all vertices. We then express both quantities in terms of quadratic forms of the pseudoinverse for graph Laplacian, based on which we develop an efficient algorithm that provides an approximation of $H_j$ for all vertices and K in nearly linear time with respect to the edge number, with high probability. Extensive experiment results on real-life and model networks validate both the efficiency and accuracy of the proposed algorithm.
Investigating Examination Behavior in Mobile Search
Authors: Yukun Zheng, Jiaxin Mao, Yiqun Liu, Mark Sanderson, Min Zhang, Shaoping Ma
Venue: WSDM
Year: 2020

Examination is one of the most important user interactions in Web search. A number of works studied examination behavior in Web search and helped researchers better understand how users allocate their attention on search engine result pages (SERPs). Compared to desktop search, mobile search has a number of differences such as fewer results on the screen. These differences bring in mobile-specific factors affecting users' examination behavior. However, there still lacks research on users' attention allocation mechanism via viewports in mobile search. Therefore, we design a lab-based study to collect user's rich interaction behavior in mobile search. Based on the collected data, we first analyze how users examine SERPs and allocate their attention to heterogeneous results. Then we investigate the effect of mobile-specific factors and other common factors on users allocating attention. Finally, we apply the findings of user attention allocation from the user study into click model construction efforts, which significantly improves the state-of-the-art click model. Our work brings insights into a better understanding of users' interaction patterns in mobile search and may benefit other mobile search-related research.
Can Deep Learning Only Be Neural Networks?
Authors: Zhi-Hua Zhou
Venue: WSDM
Year: 2020

The word "deep learning" is generally regarded as a synonym of "deep neural networks (DNNs)". In this talk, we will discuss on essentials in deep learning and claim that deep learning is not necessarily to be realized by neural networks and differentiable modules. We will then present an exploration to non-NN style deep learning, where the building blocks are non-differentiable modules and the training process does not rely on backpropagation or gradient-based adjustment. We will also talk about some recent advances and challenges in this direction of research.
Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation
Authors: Nengjun Zhu, Jian Cao, Yanchi Liu, Yang Yang, Haochao Ying, Hui Xiong
Venue: WSDM
Year: 2020

The next-item recommendation has attracted great research interests with both static and dynamic users' preferences considered. Existing approaches typically utilize user-item binary relations, and assume a flat preference distribution over items for each user. However, this assumption neglects the hierarchical discrimination between user intentions and user preferences, causing the methods have limited capacity to depict intention-specific preference. In fact, a consumer's purchasing behavior involves a natural sequential process, i.e., he/she first has an intention to buy one type of items, followed by choosing a specific item according to his/her preference under this intention. To this end, we propose a novel key-array memory network (KA-MemNN), which takes both user intentions and preferences into account for next-item recommendation. Specifically, the user behavioral intention tendency is determined through key addressing. Further, each array outputs an intention-specific preference representation of a user. Then, the degree of user's behavioral intention tendency and intention-specific preference representation are combined to form a hierarchical representation of a user. This representation is further utilized to replace the static profile of users in traditional matrix factorization for the purposes of reasoning. The experimental results on real-world data demonstrate the advantages of our approach over state-of-the-art methods.