OpenRaiser/Intern-Atlas huggingface dataset:
Integrating Local Context and Global Cohesiveness for Open Information Extraction
Authors: Qi Zhu, Xiang Ren, Jingbo Shang, Yu Zhang, Ahmed El-Kishky, Jiawei Han
Venue: WSDM
Year: 2019

Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences. These relation tuples are not confined to a predefined schema for the relations of interests. However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions. In this paper, we propose a novel Open IE system, called ReMine, which integrates local context signals and global structural signals in a unified, distant-supervision framework. Leveraging facts from external knowledge bases as supervision, the new system can be applied to many different domains to facilitate sentence-level tuple extractions using corpus-level statistics. Our system operates by solving a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of tuples extracted from individual sentences with a translating-based objective. Learning the two subtasks jointly helps correct errors produced in each subtask so that they can mutually enhance each other. Experiments on two real-world corpora from different domains demonstrate the effectiveness, generality, and robustness of ReMine when compared to state-of-the-art open IE systems.
Listwise Learning to Rank by Exploring Unique Ratings
Authors: Xiaofeng Zhu, Diego Klabjan
Venue: WSDM
Year: 2020

In this paper, we propose new listwise learning-to-rank models that mitigate the shortcomings of existing ones. Existing listwise learning-to-rank models are generally derived from the classical Plackett-Luce model, which has three major limitations. (1) Its permutation probabilities overlook ties, i.e., a situation when more than one document has the same rating with respect to a query. This can lead to imprecise permutation probabilities and inefficient training because of selecting documents one by one. (2) It does not favor documents having high relevance. (3) It has a loose assumption that sampling documents at different steps is independent. To overcome the first two limitations, we model ranking as selecting documents from a candidate set based on unique rating levels in decreasing order. The number of steps in training is determined by the number of unique rating levels. More specifically, in each step, we apply multiple multi-class classification tasks to a document candidate set and choose all documents that have the highest rating from the document set. This is in contrast to taking one document step by step in the classical Plackett-Luce model. Afterward, we remove all of the selected documents from the document set and repeat until the remaining documents all have the lowest rating. We propose a new loss function and associated four models for the entire sequence of weighted classification tasks by assigning high weights to the selected documents with high ratings for optimizing Normalized Discounted Cumulative Gain (NDCG). To overcome the final limitation, we further propose a novel and efficient way of refining prediction scores by combining an adapted Vanilla Recurrent Neural Network (RNN) model with pooling given selected documents at previous steps. We encode all of the documents already selected by an RNN model. In a single step, we rank all of the documents with the same ratings using the last cell of the RNN multiple times. We have implemented our models using three settings: neural networks, neural networks with gradient boosting, and regression trees with gradient boosting. We have conducted experiments on four public datasets. The experiments demonstrate that the models notably outperform state-of-the-art learning-to-rank models.
ConvERSe'20: The WSDM 2020 Workshop on Conversational Systems for E-Commerce Recommendations and Search
Authors: Eugene Agichtein, Dilek Hakkani-Tür, Surya Kallumadi, Shervin Malmasi
Venue: WSDM
Year: 2020

Conversational systems have improved dramatically recently, and are receiving increasing attention in academic literature. These systems are also becoming adapted in E-Commerce due to increased integration of E-Commerce search and recommendation source with virtual assistants such as Alexa, Siri, and Google assistant. However, significant research challenges remain spanning areas of dialogue systems, spoken natural language processing, human-computer interaction, and search and recommender systems, which all are exacerbated with demanding requirements of E-Commerce. The purpose of this workshop is to bring together researchers and practitioners in the areas of conversational systems, human-computer interaction, information retrieval, and recommender systems. Bringing diverse research areas together into a single workshop would accelerate progress on adapting conversation systems to the E-Commerce domain, to set a research agenda, to examine how to build and share data sets, and to establish common evaluation metrics and benchmarks to drive research progress.
Impact of Online Job Search and Job Reviews on Job Decision
Authors: Faiz Ahamad
Venue: WSDM
Year: 2020

Online platforms such as LinkedIn or specialized platforms such as Glassdoor are widely used by job seekers before applying for the job. These web platforms have rating and reviews about employer and jobs. Hence a job seeker do online search for the employer, before applying for the job. They try to find if the employer and job is good for them or not, what are the pros and cons of working there etc. Therefore, these reviews and ratings have an impact on job seekers decision as it portrays the pros and cons of working in a particular firm. Hence, the main objective of this study is main objective of this study is to find how the job seekers search for online employer reviews and the impact of these reviews on employer attractiveness and job pursuit intention. The other objective is to find the most crucial job factors that are given priority by the employee. For this, the study is proposed to be conducted in two stages, first, collecting data from the website Glassdoor, having 600000 companies' reviews. In the second stage, conducting an experimental study to examine the influence of job attributes (high vs. low) and employer rating (high vs. low) on job choice and employer attractiveness.
User Intent Inference for Web Search and Conversational Agents
Authors: Ali Ahmadvand
Venue: WSDM
Year: 2020

User intent understanding is a crucial step in designing both conversational agents and search engines. Detecting or inferring user intent is challenging, since the user utterances or queries can be short, ambiguous, and contextually dependent. To address these research challenges, my thesis work focuses on: 1) Utterance topic and intent classification for conversational agents 2) Query intent mining and classification for Web search engines, focusing on the e-commerce domain. To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances. For the second research topic, I plan to extend the existing state of the art methods in Web search intent prediction to the e-commerce domain, via: 1) Developing a joint learning model to predict search queries' intents and the product categories associated with them, 2) Discovering new hidden users' intents. All the models will be evaluated on the real queries available from a major e-commerce site search engine. The results from these studies can be leveraged to improve performance of various tasks such as natural language understanding, query scoping, query suggestion, and ranking, resulting in an enriched user experience.
Language-Agnostic Representation Learning for Product Search on E-Commerce Platforms
Authors: Aman Ahuja, Nikhil Rao, Sumeet Katariya, Karthik Subbian, Chandan K. Reddy
Venue: WSDM
Year: 2020

Product search forms an indispensable component of any e-commerce service, and helps customers find products of their interest from a large catalog on these websites. When products that are irrelevant to the search query are surfaced, it leads to a poor customer experience, thus reducing user trust and increasing the likelihood of churn. While identifying and removing such results from product search is crucial, doing so is a burdensome task that requires large amounts of human annotated data to train accurate models. This problem is exacerbated when products are cross-listed across countries that speak multiple languages, and customers specify queries in multiple languages and from different cultural contexts. In this work, we propose a novel multi-lingual multi-task learning framework, to jointly train product search models on multiple languages, with limited amount of training data from each language. By aligning the query and product representations from different languages into a language-independent vector space of queries and products, respectively, the proposed model improves the performance over baseline search models in any given language. We evaluate the performance of our model on real data collected from a leading e-commerce service. Our experimental evaluation demonstrates up to 23% relative improvement in the classification F1-score compared to the state-of-the-art baseline models.
Recurrent Attention Walk for Semi-supervised Classification
Authors: Uchenna Akujuobi, Qiannan Zhang, Yufei Han, Xiangliang Zhang
Venue: WSDM
Year: 2020

In this paper, we study the graph-based semi-supervised learning for classifying nodes in attributed networks, where the nodes and edges possess content information. Recent approaches like graph convolution networks and attention mechanisms have been proposed to ensemble the first-order neighbors and incorporate the relevant neighbors. However, it is costly (especially in memory) to consider all neighbors without a prior differentiation. We propose to explore the neighborhood in a reinforcement learning setting and find a walk path well-tuned for classifying the unlabelled target nodes. We let an agent (of node classification task) walk over the graph and decide where to move to maximize classification accuracy. We define the graph walk as a partially observable Markov decision process (POMDP). The proposed method is flexible for working in both transductive and inductive setting. Extensive experiments on four datasets demonstrate that our proposed method outperforms several state-of-the-art methods. Several case studies also illustrate the meaningful movement trajectory made by the agent.
Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding
Authors: Sambaran Bandyopadhyay, Lokesh N, Saley Vishal Vivek, M. Narasimha Murty
Venue: WSDM
Year: 2020

Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks. Nodes in a network exhibit community structure and most of the network embedding algorithms work well when the nodes, along with their attributes, adhere to the community structure of the network. But real life networks come with community outlier nodes, which deviate significantly in terms of their link structure or attribute similarities from the other nodes of the community they belong to. These outlier nodes, if not processed carefully, can even affect the embeddings of the other nodes in the network. Thus, a node embedding framework for dealing with both the link structure and attributes in the presence of outliers in an unsupervised setting is practically important. In this work, we propose a deep unsupervised autoencoders based solution which minimizes the effect of outlier nodes while generating the network embedding. We use both stochastic gradient descent and closed form updates for faster optimization of the network parameters. We further explore the role of adversarial learning for this task, and propose a second unsupervised deep model which learns by discriminating the structure and the attribute based embeddings of the network and minimizes the effect of outliers in a coupled way. Our experiments show the merit of these deep models to detect outliers and also the superiority of the generated network embeddings for different downstream mining tasks. To the best of our knowledge, these are the first unsupervised non linear approaches that reduce the effect of the outlier nodes while generating Network Embedding.
personality2vec: Enabling the Analysis of Behavioral Disorders in Social Networks
Authors: Amin Beheshti, Vahid Moraveji Hashemi, Shahpar Yakhchi, Hamid Reza Motahari-Nezhad, Seyed Mohssen Ghafari, Jian Yang
Venue: WSDM
Year: 2020

Enabling the analysis of behavioral disorders over time in social networks, can help in suicide prevention, (school) bullying detection and extremist/criminal activity prediction. In this paper, we present a novel data analytics pipeline to enable the analysis of patterns of behavioral disorders on social networks. We present a Social Behavior Graph (sbGraph) model, to enable the analysis of factors that are driving behavior disorders over time. We use the golden standards in personality, behavior and attitude to build a domain specific Knowledge Base (KB). We use this domain knowledge to design cognitive services to automatically contextualize the raw social data and to prepare them for behavioral analytics. Then we introduce a pattern-based word embedding technique, namely personality2vec, on each feature extracted to build the sbGraph. The goal is to use mathematical embedding from a space with a dimension per feature to a continuous vector space which can be mapped to classes of behavioral disorders (such as cyber-bullying and radicalization) in the domain specific KB. We implement an interactive dashboard to enable social network analysts to analyze and understand the patterns of behavioral disorders over time. We focus on a motivating scenario in Australian government's office of the e-Safety commissioner, where the goal is to empowering all citizens to have safer, more positive experiences online.
Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning
Authors: Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, Huan Liu
Venue: WSDM
Year: 2020

Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which simultaneously recommends relevant items and counters private-attribute inference attacks. The key idea of our approach is to formulate this problem as an adversarial learning problem with two main components: the private attribute inference attacker, and the Bayesian personalized recommender. The attacker seeks to infer users' private-attribute information according to their items list and recommendations. The recommender aims to extract users' interests while employing the attacker to regularize the recommendation process. Experiments show that the proposed model both preserves the quality of recommendation service and protects users against private-attribute inference attacks.
Temporal Pattern of Retweet(s) Help to Maximize Information Diffusion in Twitter
Authors: Ayan Kumar Bhowmick
Venue: WSDM
Year: 2020

Twitter is currently a popular microblogging platform for spread of information by users in the form of tweet messages. Such tweets are shared with followers of the seed user who may reshare it with their own set of followers. Long chain of such retweets form cascades. For effective diffusion of information through such Twitter cascades, we identify two different objectives based on using temporal sequence of retweets. Firstly, we aim to infer the structure of influence trees of Twitter cascades, denoting the who-influenced-whom relationship among retweeting users in the cascade, that can play a significant role in identifying critical paths in the network for information dissemination. The constructed trees closely resemble ground truth influence trees of empirical cascades with high retweet count. Secondly, we propose a fast and efficient algorithm for detection of influential users by identifying anchor nodes from temporal retweet sequence. Identification of such a diverse set of influential users enable a faster diffusion of tweets to a large and diverse population, when targeted as seeds thereby maximizing the influence spread, facilitating several applications including viral marketing, disease control and news dissemination.
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding
Authors: Ayan Kumar Bhowmick, Koushik Meneni, Maximilien Danisch, Jean-Loup Guillaume, Bivas Mitra
Venue: WSDM
Year: 2020

Network embedding, that aims to learn low-dimensional vector representation of nodes such that the network structure is preserved, has gained significant research attention in recent years. However, most state-of-the-art network embedding methods are computationally expensive and hence unsuitable for representing nodes in billion-scale networks. In this paper, we present LouvainNE, a hierarchical clustering approach to network embedding. Precisely, we employ Louvain, an extremely fast and accurate community detection method, to build a hierarchy of successively smaller subgraphs. We obtain representations of individual nodes in the original graph at different levels of the hierarchy, then we aggregate these representations to learn the final embedding vectors. Our theoretical analysis shows that our proposed algorithm has quasi-linear run-time and memory complexity. Our extensive experimental evaluation, carried out on multiple real-world networks of different scales, demonstrates both (i) the scalability of our proposed approach that can handle graphs containing tens of billions of edges, as well as (ii) its effectiveness in performing downstream network mining tasks such as network reconstruction and node classification.
A Stochastic Treatment of Learning to Rank Scoring Functions
Authors: Sebastian Bruch, Shuguang Han, Michael Bendersky, Marc Najork
Venue: WSDM
Year: 2020

Learning to Rank, a central problem in information retrieval, is a class of machine learning algorithms that formulate ranking as an optimization task. The objective is to learn a function that produces an ordering of a set of documents in such a way that the utility of the entire ordered list is maximized. Learning-to-rank methods do so by learning a function that computes a score for each document in the set. A ranked list is then compiled by sorting documents according to their scores. While such a deterministic mapping of scores to permutations makes sense during inference where stability of ranked lists is required, we argue that its greedy nature during training leads to less robust models. This is particularly problematic when the loss function under optimization---in agreement with ranking metrics---largely penalizes incorrect rankings and does not take into account the distribution of raw scores. In this work, we present a stochastic framework where, instead of a deterministic derivation of permutations from raw scores, permutations are sampled from a distribution defined by raw scores. Our proposed sampling method is differentiable and works well with gradient descent optimizers. We analytically study our proposed method and demonstrate when and why it leads to model robustness. We also show empirically, through experiments on publicly available learning-to-rank datasets, that the application of our proposed method to a class of ranking loss functions leads to significant model quality improvements.
SUM'20: State-based User Modelling
Authors: Sahan Bulathwela, María Pérez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin de la Higuera, John Shawe-Taylor, Emine Yilmaz
Venue: WSDM
Year: 2020

Capturing and effectively utilising user states and goals is becoming a timely challenge for successfully leveraging intelligent and usercentric systems in differentweb search and data mining applications. Examples of such systems are conversational agents, intelligent assistants, educational and contextual information retrieval systems, recommender/match-making systems and advertising systems, all of which rely on identifying the user state in order to provide the most relevant information and assist users in achieving their goals. There has been, however, limited work towards building such state-aware intelligent learning mechanisms. Hence, devising information systems that can keep track of the user's state has been listed as one of the grand challenges to be tackled in the next few years [1]. It is thus timely to organize a workshop that re-visits the problem of designing and evaluating state-aware and user-centric systems, ensuring that the community (spanning academic and industrial backgrounds) works together to tackle these challenges.
Popularity Prediction on Social Platforms with Coupled Graph Neural Networks
Authors: Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, Xueqi Cheng
Venue: WSDM
Year: 2020

Predicting the popularity of online content on social platforms is an important task for both researchers and practitioners. Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction. However, most existing methods are less effective to precisely capture the cascading effect in information diffusion, in which early adopters try to activate potential users along the underlying network. In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks for popularity prediction. We propose to capture the cascading effect explicitly, modeling the activation state of a target user given the activation state and influence of his/her neighbors. To achieve this goal, we propose a novel method, namely CoupledGNN, which uses two coupled graph neural networks to capture the interplay between node activation states and the spread of influence. By stacking graph neural network layers, our proposed method naturally captures the cascading effect along the network in a successive manner. Experiments conducted on both synthetic and real-world Sina Weibo datasets demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction.