OpenRaiser/Intern-Atlas huggingface dataset:
Designing the Cogno-Web Observatory: To Characterize the Dynamics of Online Social Cognition
Authors: Raksha Pavagada Subbanarasimha
Venue: WSDM
Year: 2019

Our understanding of the web has been evolving from a large database of information to a Socio - Cognitive Space, where humans are not just using the web but participating in the web. World wide web has evolved into the largest source of information in the history, and it continues to grow without any known agenda. The web needs to be observed and studied to understand various impacts of it on the society (both positive and negative) and shape the future of the web and the society. This gave rise to the global grid of Web Observatories which focus and observe various aspects of the web. Web Observatories aim to share and collaborate various data sets, analysis tools and applications with all web observatories across the world. We plan to design and develop a Web Observatory called to observe and understand online social cognition. We propose that the social media on the web is acting as a Marketplace of Opinions where multiple users with differing interests exchange opinions. For a given trending topic on social media, we propose a model to identify the Signature of the trending topic which characterizes the discourse around the topic.
Multi-Representation Fusion Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Authors: Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, Rui Yan
Venue: WSDM
Year: 2019

We consider context-response matching with multiple types of representations for multi-turn response selection in retrieval-based chatbots. The representations encode semantics of contexts and responses on words, n-grams, and sub-sequences of utterances, and capture both short-term and long-term dependencies among words. With such a number of representations in hand, we study how to fuse them in a deep neural architecture for matching and how each of them contributes to matching. To this end, we propose a multi-representation fusion network where the representations can be fused into matching at an early stage, at an intermediate stage, or at the last stage. We empirically compare different representations and fusing strategies on two benchmark data sets. Evaluation results indicate that late fusion is always better than early fusion, and by fusing the representations at the last stage, our model significantly outperforms the existing methods, and achieves new state-of-the-art performance on both data sets. Through a thorough ablation study, we demonstrate the effect of each representation to matching, which sheds light on how to select them in practical systems.
Attending to What Matters
Authors: Jaime Teevan
Venue: WSDM
Year: 2019

Online services are increasingly intelligent. They evolve intelligently through A/B testing and experimentation, employ artificial intelligence in their core functionality using machine learning, and seamlessly engage human intelligence by connecting people in a low-friction manner. All of this has resulted in incredibly engaging experiences -- but not particularly productive ones. As more and more of people's most important tasks move online we need to think carefully about the underlying influence online services have on people's ability to attend to what matters to them. There is an opportunity to use intelligence for this to do more than just not distract people and actually start helping people attend to what matters even better than they would otherwise. This presentation explores the ways we might make it as compelling and easy to start an important task as it is to check social media.
On the Complexity of Opinions and Online Discussions
Authors: Utkarsh Upadhyay, Abir De, Aasish Pappu, Manuel Gomez-Rodriguez
Venue: WSDM
Year: 2019

In an increasingly polarized world, demagogues who reduce complexity down to simple arguments based on emotion are gaining in popularity. Are opinions and online discussions falling into demagoguery? In this work, we aim to provide computational tools to investigate this question and, by doing so, explore the nature and complexity of online discussions and their space of opinions, uncovering where each participant lies. More specifically, we present a modeling framework to construct latent representations of opinions in online discussions which are consistent with human judgments, as measured by online voting. If two opinions are close in the resulting latent space of opinions, it is because humans think they are similar. Our framework is theoretically grounded and establishes a surprising connection between opinion and voting models and the sign-rank of matrices. Moreover, it also provides a set of practical algorithms to both estimate the dimensionality of the latent space of opinions and infer where opinions expressed by the participants of an online discussion lie in this space. Experiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports, and the Newsroom app show that many discussions are multisided, reveal a positive correlation between the complexity of a discussion, its linguistic diversity and its level of controversy, and show that our framework may be able to circumvent language nuances such as sarcasm or humor by relying on human judgments instead of textual analysis.
CluWords: Exploiting Semantic Word Clustering Representation for Enhanced Topic Modeling
Authors: Felipe Viegas, Sérgio D. Canuto, Christian Gomes, Washington Luiz, Thierson Rosa, Sabir Ribas, Leonardo Rocha, Marcos André Gonçalves
Venue: WSDM
Year: 2019

In this paper, we advance the state-of-the-art in topic modeling by means of a new document representation based on pre-trained word embeddings for non-probabilistic matrix factorization. Specifically, our strategy, called CluWords, exploits the nearest words of a given pre-trained word embedding to generate meta-words capable of enhancing the document representation, in terms of both, syntactic and semantic information. The novel contributions of our solution include: (i)the introduction of a novel data representation for topic modeling based on syntactic and semantic relationships derived from distances calculated within a pre-trained word embedding space and (ii)the proposal of a new TF-IDF-based strategy, particularly developed to weight the CluWords. In our extensive experimentation evaluation, covering 12 datasets and 8 state-of-the-art baselines, we exceed (with a few ties) in almost cases, with gains of more than 50% against the best baselines (achieving up to 80% against some runner-ups). Finally, we show that our method is able to improve document representation for the task of automatic text classification.
Pleasant Route Suggestion based on Color and Object Rates
Authors: Shoko Wakamiya, Panote Siriaraya, Yihong Zhang, Yukiko Kawai, Eiji Aramaki, Adam Jatowt
Venue: WSDM
Year: 2019

For a tourist who wishes to stroll in an unknown city, it is useful to have a recommendation of not just the shortest routes but also routes that are pleasant. This paper demonstrates a system that provides pleasant route recommendation. Currently, we focus on routes that have much green and bright views. The system measures pleasure scores by extracting colors or objects in Google Street View panorama images and re-ranks shortest paths in the order of the computed pleasure scores. The current prototype provides route recommendation for city areas in Tokyo, Kyoto and San Francisco.
How is Attention Allocated?: Data-Driven Studies of Popularity and Engagement in Online Videos
Authors: Siqi Wu
Venue: WSDM
Year: 2019

The share of videos on Internet traffic has been growing, e.g., people are now spending a billion hours watching YouTube videos every day. Therefore, understanding how videos capture attention on a global scale is also of growing importance for both research and practice. In online platforms, people can interact with videos in different ways -- there are behaviors of active participation (watching, commenting, and sharing) and that of passive consumption (viewing). In this paper, we take a data-driven approach to studying how human attention is allocated in online videos with respect to both active and passive behaviors. We first investigate the active interaction behaviors by proposing a novel metric to represent the aggregate user engagement on YouTube videos. We show this metric is correlated with video quality, stable over lifetime, and predictable before video's upload. Next, we extend the line of work on modelling video view counts by disentangling the effects of two dominant traffic sources -- related videos and YouTube search. Findings from this work can help content producers to create engaging videos and hosting platforms to optimize advertising strategies, recommender systems, and many more applications.
MSA: Jointly Detecting Drug Name and Adverse Drug Reaction Mentioning Tweets with Multi-Head Self-Attention
Authors: Chuhan Wu, Fangzhao Wu, Zhigang Yuan, Junxin Liu, Yongfeng Huang, Xing Xie
Venue: WSDM
Year: 2019

Twitter is a popular social media platform for information sharing and dissemination. Many Twitter users post tweets to share their experiences about drugs and adverse drug reactions. Automatic detection of tweets mentioning drug names and adverse drug reactions at a large scale has important applications such as pharmacovigilance. However, detecting drug name and adverse drug reaction mentioning tweets is very challenging, because tweets are usually very noisy and informal, and there are massive misspellings and user-created abbreviations for these mentions. In addition, these mentions are usually context dependent. In this paper, we propose a neural approach with hierarchical tweet representation and multi-head self-attention mechanism to jointly detect tweets mentioning drug names and adverse drug reactions. In order to alleviate the influence of massive misspellings and user-created abbreviations in tweets, we propose to use a hierarchical tweet representation model to first learn word representations from characters and then learn tweet representations from words. In addition, we propose to use multi-head self-attention mechanism to capture the interactions between words to fully model the contexts of tweets. Besides, we use additive attention mechanism to select the informative words to learn more informative tweet representations. Experimental results validate the effectiveness of our approach.
Weakly Supervised Co-Training of Query Rewriting andSemantic Matching for e-Commerce
Authors: Rong Xiao, Jianhui Ji, Baoliang Cui, Haihong Tang, Wenwu Ou, Yanghua Xiao, Jiwei Tan, Xuan Ju
Venue: WSDM
Year: 2019

Relevance is the core problem of a search engine, and one of the main challenges is the vocabulary gap between user queries and documents. This problem is more serious in e-commerce, because language in product titles is more professional. Query rewriting and semantic matching are two key techniques to bridge the semantic gap between them to improve relevance. Recently, deep neural networks have been successfully applied to the two tasks and enhanced the relevance performance. However, such approaches suffer from the sparseness of training data in e-commerce scenario. In this study, we investigate the instinctive connection between query rewriting and semantic matching tasks, and propose a co-training framework to address the data sparseness problem when training deep neural networks. We first build a huge unlabeled dataset from search logs, on which the two tasks can be considered as two different views of the relevance problem. Then we iteratively co-train them via labeled data generated from this unlabeled set to boost their performance simultaneously. We conduct a series of offline and online experiments on a real-world e-commerce search engine, and the results demonstrate that the proposed method improves relevance significantly.
User Behavior Modeling for Web Image Search
Authors: Xiaohui Xie
Venue: WSDM
Year: 2019

Web-based image search engines differ from Web search engines greatly. The intents or goals behind human interactions with image search engines are different. In image search, users mainly search images instead of Web pages or online services. It is essential to know why people search for images because user satisfaction may vary as intent varies. Furthermore, image search engines show results differently. For example, grid-based placement is used in image search instead of the linear result list, so that users can browse result list both vertically and horizontally. Different user intents and system UIs lead to different user behavior. Thus, it is hard to apply standard user behavior models developed for general Web search to image search. To better understand user intent and behavior in image search scenarios, we plan to conduct the lab-based user study, field study and commercial search log analysis. We then propose user behavior models based on the observation from data analysis to improve the performance of Web image search engines.
Deep Learning for Matching in Search and Recommendation
Authors: Jun Xu, Xiangnan He, Hang Li
Venue: WSDM
Year: 2019

Matching is the key problem in search and recommendation, that is to measure the relevance of a document to a query or the interest of a user on an item. Previously, machine learning methods have been exploited to address the problem, which learn a matching function from labeled data, also referred to as "learning to match". In recent years, deep learning has been successfully applied to matching and significant progresses have been made. Deep semantic matching models for search and neural collaborative filtering models for recommendation are becoming the state-of-the-art technologies. The key to the success of the deep learning approach is its strong ability in learning of representations and generalization of matching patterns from raw data (e.g., queries, documents, users, and items, particularly in their raw forms). In this tutorial, we aim to give a comprehensive survey on recent progress in deep learning for matching in search and recommendation. Our tutorial is unique in that we try to give a unified view on search and recommendation. In this way, we expect researchers from the two fields can get deep understanding and accurate insight on the spaces, stimulate more ideas and discussions, and promote developments of technologies. The tutorial mainly consists of three parts. Firstly, we introduce the general problem of matching, which is fundamental in both search and recommendation. Secondly, we explain how traditional machine learning techniques are utilized to address the matching problems in search and recommendation. Lastly, we elaborate how deep learning can be effectively used to solve the matching problems in both tasks.
The Local Closure Coefficient: A New Perspective On Network Clustering
Authors: Hao Yin, Austin R. Benson, Jure Leskovec
Venue: WSDM
Year: 2019

The phenomenon of edge clustering in real-world networks is a fundamental property underlying many ideas and techniques in network science. Clustering is typically quantified by the clustering coefficient, which measures the fraction of pairs of neighbors of a given center node that are connected. However, many common explanations of edge clustering attribute the triadic closure to a head node instead of the center node of a length-2 path; for example, a friend of my friend is also my friend. While such explanations are common in network analysis, there is no measurement for edge clustering that can be attributed to the head node. Here we develop local closure coefficients as a metric quantifying head-node-based edge clustering. We define the local closure coefficient as the fraction of length-2 paths emanating from the head node that induce a triangle. This subtle difference in definition leads to remarkably different properties from traditional clustering coefficients. We analyze correlations with node degree, connect the closure coefficient to community detection, and show that closure coefficients as a feature can improve link prediction.
A Simple Convolutional Generative Network for Next Item Recommendation
Authors: Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Xiangnan He
Venue: WSDM
Year: 2019

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we introduce a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range item dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the next item recommendation task. It accordingly can be used as a powerful recommendation baseline to beat in future, especially when there are long sequences of user feedback.
Listwise vs Pagewise: Towards Better Ranking Strategies for Heterogeneous Search Results
Authors: Junqi Zhang
Venue: WSDM
Year: 2019

As heterogeneous verticals account for more and more in search engines, users' preference of search results is largely affected by their presentations. Apart from texts, multimedia information such as images and videos has been widely adopted as it makes the search engine result pages (SERPs) more informative and attractive. It is more proper to regard the SERP as an information union, not separate search results because they interact with each other. Considering these changes in search engines, we plan to better exploit the contents of search results displayed on SERPs through deep neural networks and formulate the pagewise optimization of SERPs as a reinforcement learning problem.
ATAR: Aspect-Based Temporal Analog Retrieval System for Document Archives
Authors: Yating Zhang, Adam Jatowt, Sourav S. Bhowmick, Yuji Matsumoto
Venue: WSDM
Year: 2019

In recent years, we have witnessed a rapid increase of text content stored in digital archives such as newspaper archives or web archives. With the passage of time, it is however difficult to effectively perform search within such collections due to vocabulary and context change. In this paper, we present a system that helps to find analogical terms across temporal text collections by applying non-linear transformation. We implement two approaches for analog retrieval where one of them allows users to also input an aspect term specifying particular perspective of a query. The current prototype system permits temporal analog search across two different time periods based on New York Times Annotated Corpus.
SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks
Authors: Chuxu Zhang, Ananthram Swami, Nitesh V. Chawla
Venue: WSDM
Year: 2019

Representation learning in heterogeneous networks faces challenges due to heterogeneous structural information of multiple types of nodes and relations, and also due to the unstructured attribute or content (e.g., text) associated with some types of nodes. While many recent works have studied homogeneous, heterogeneous, and attributed networks embedding, there are few works that have collectively solved these challenges in heterogeneous networks. In this paper, we address them by developing a Semantic-aware Heterogeneous Network Embedding model (SHNE). SHNE performs joint optimization of heterogeneous SkipGram and deep semantic encoding for capturing both heterogeneous structural closeness and unstructured semantic relations among all nodes, as function of node content, that exist in the network. Extensive experiments demonstrate that SHNE outperforms state-of-the-art baselines in various heterogeneous network mining tasks, such as link prediction, document retrieval, node recommendation, relevance search, and class visualization.
FaceOff: Assisting the Manifestation Design of Web Graphical User Interface
Authors: Shuyu Zheng, Ziniu Hu, Yun Ma
Venue: WSDM
Year: 2019

Designing desirable and aesthetical manifestation of web graphic user interfaces (GUI) is a challenging task for web developers. After determining a web page's content, developers usually refer to existing pages, and adapt the styles from desired pages into the target one. However, it is not only difficult to find appropriate pages to exhibit the target page's content, but also tedious to incorporate styles from different pages harmoniously in the target page. To tackle these two issues, we propose FaceOff, a data-driven automation system that assists the manifestation design of web GUI. FaceOff constructs a repository of web GUI templates based on 15,491 web pages from popular websites and professional design examples. Given a web page for designing manifestation, FaceOff first segments it into multiple blocks, and retrieves GUI templates in the repository for each block. Subsequently, FaceOff recommends multiple combinations of templates according to a Convolutional Neural Network (CNN) based style-embedding model, which makes the recommended style combinations diverse and accordant. We demonstrate that FaceOff can retrieve suitable GUI templates with well-designed and harmonious style, and thus alleviate the developer efforts.
The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)
Authors: Xiangmin Zhou, Ji Zhang, Yanchun Zhang
Venue: WSDM
Year: 2019

With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Through many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics.