OpenRaiser/Intern-Atlas huggingface dataset:
R2SIGTP: a Novel Real-Time Recommendation System with Integration of Geography and Temporal Preference for Next Point-of-Interest
Authors: Xu Jiao, Yingyuan Xiao, Wenguang Zheng, Hongya Wang, Youzhi Jin
Venue: WWW
Year: 2019

With the rapid development of location-based social networks (LBSNs), point of interest (POI) recommendation has become an important way to meet users' personalized demands. The aim of POI recommendation is to provide personalized recommendation of POIs for mobile users. However, traditional POI recommendation systems cannot satisfy users' personalized demands. The reason is that the traditional POI recommendation system cannot recommend the next POI to a user based on the user's context information. Also, the traditional POI recommendation system provides no real-time guarantee on performance. In this demo, we propose a novel real-time next POI recommendation system named R2SIGTP which provides more personalized real-time recommendation compared with existing ones. Our system has the following advantages: 1) it has real-time performance; 2) it uses a unified approach to integrate geographic and preference information; 3) it considers the feedback of each single user to provide more personalized recommendation. We have implemented our system. R2SIGTP is easy to use and can be used by the mobile terminal's browser to recommend the next POI to the user in real-time based on the automatically identified user location and current time. The experimental results on real-world LBSNs show that R2SIGTP's performance is satisfactory.
A Novel Generative Topic Embedding Model by Introducing Network Communities
Authors: Di Jin, Jiantao Huang, Pengfei Jiao, Liang Yang, Dongxiao He, Françoise Fogelman-Soulié, Yuxiao Huang
Venue: WWW
Year: 2019

Topic models have many important applications in fields such as Natural Language Processing. Topic embedding modelling aims at introducing word and topic embeddings into topic models to describe correlations between topics. Existing topic embedding methods use documents alone, which suffer from the topical fuzziness problem brought by the introduction of embeddings of semantic fuzzy words, e.g. polysemous words or some misleading academic terms. Links often exist between documents which form document networks. The use of links may alleviate this semantic fuzziness, but they are sparse and noisy which may meanwhile mislead topics. In this paper, we utilize community structure to solve these problems. It can not only alleviate the topical fuzziness of topic embeddings since communities are often believed to be topic related, but also can overcome the drawbacks brought by the sparsity and noise of networks (because community is a high-order network information). We give a new generative topic embedding model which incorporates documents (with topics) and network (with communities) together, and uses probability transition to describe the relationship between topics and communities to make it robust when topics and communities do not match. An efficient variational inference algorithm is then proposed to learn the model. We validate the superiority of our new approach on two tasks, document classifications and visualization of topic embeddings, respectively.
Lightning Talk - Ruuh: A Conversational Social Agent
Authors: Meghana Joshi, Sonam Damani, Khyatti Gupta, Nitya Raviprakash
Venue: WWW
Year: 2019

Dialogue systems and conversational agents are becoming increasingly popular in the modern society but building an agent capable of holding intelligent conversation with its users is a challenging problem for artificial intelligence. In this talk, we share challenges and learnings from our journey of building a deep learning based conversational social agent called ”Ruuh” (m.me/Ruuh) developed by a team at Microsoft India to converse on a wide range of topics. The authors are co-creators of Ruuh and the original paper was presented in NeurIPS 2018 Demonstration Track by two of the authors. As a social agent, Ruuh needs to think beyond the utilitarian notion of merely generating ”relevant” responses and meet a wider range of user social needs. The agent also needs to detect and respond to abusive language, sensitive topics and trolling behavior of users. Some of the above objectives pose significant research challenges in the areas of NLP, IR and AI. Our agent has interacted with over 2 million real world users till date which has generated over 150 million user conversations. We intend to walk the audience through our journey of overcoming several research challenges to become the most popular social agent in India.
Modeling and Mining Feature-Rich Networks
Authors: Rushed Kanawati, Martin Atzmueller
Venue: WWW
Year: 2019

In the field of web mining and web science, as well as data science and data mining there has been a lot of interest in the analysis of (social) networks. With the growing complexity of heterogeneous data, feature-rich networks have emerged as a powerful modeling approach: They capture data and knowledge at different scales from multiple heterogeneous data sources, and allow the mining and analysis from different perspectives. The challenge is to devise novel algorithms and tools for the analysis of such networks. This tutorial provides a unified perspective on feature-rich networks, focusing on different modeling approaches, in particular multiplex and attributed networks. It outlines important principles, methods, tools and future research directions in this emerging field.
Incentive-Compatible Learning of Reserve Prices for Repeated Auctions
Authors: Yash Kanoria, Hamid Nazerzadeh
Venue: WWW
Year: 2019

Motivated by online advertising market, we consider a seller who repeatedly sells ex ante identical items via the second-price auction. Buyers’ valuations for each item are drawn i.i.d. from a distribution F that is unknown to the seller. We find that if the seller attempts to dynamically update a common reserve price based on the bidding history, this creates an incentive for buyers to shade their bids, which can hurt revenue. When there is more than one buyer, incentive compatibility can be restored by using personalized reserve prices, where the personal reserve price for each buyer is set using the historical bids of other buyers. In addition, we use a lazy allocation rule, so that buyers do not benefit from raising the prices of their competitors. Such a mechanism asymptotically achieves the expected revenue obtained under the static Myerson optimal auction for F. Further, if valuation distributions differ across bidders, the loss relative to the Myerson benchmark is only quadratic in the size of such differences. We extend our results to a contextual setting where the valuations of the buyers depend on observed features of the items.
MARINE: Multi-relational Network Embeddings with Relational Proximity and Node Attributes
Authors: Ming-Han Feng, Chin-Chi Hsu, Cheng-Te Li, Mi-Yen Yeh, Shou-De Lin
Venue: WWW
Year: 2019

Network embedding aims at learning an effective vector transformation for entities in a network. We observe that there are two diverse branches of network embedding: for homogeneous graphs and for multi-relational graphs. This paper then proposes MARINE, a unified embedding framework for both homogeneous and multi-relational networks to preserve both the proximity and relation information. We also extend the framework to incorporate existing features of nodes in a graph, which can further be exploited for the ensemble of embedding. Our solution possesses complexity linear to the number of edges, which is suitable for large-scale network applications. Experiments conducted on several real-world network datasets, along with applications in link prediction and multi-label classification, exhibit the superiority of our proposed MARINE.
Building a Knowledge Graph for the Air Traffic Management Community
Authors: Richard M. Keller
Venue: WWW
Year: 2019

Historically, most of the focus in the knowledge graph community has been on the support for web, social network, or product search applications. This paper describes some of our experience in developing a large-scale applied knowledge graph for a more technical audience with more specialized information access and analysis needs – the air traffic management community. We describe ATMGRAPH, a knowledge graph created by integrating various sources of structured aviation data, provided in large part by US federal agencies. We review some of the practical challenges we faced in creating this knowledge graph.
Privacy-preserving Data Mining in Industry
Authors: Krishnaram Kenthapadi, Ilya Mironov, Abhradeep Thakurta
Venue: WWW
Year: 2019

Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple’s differential privacy deployment for iOS / macOS, Google’s RAPPOR, LinkedIn Salary, and Microsoft’s differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
Opico: A Study of Emoji-first Communication in a Mobile Social App
Authors: Sujay Khandekar, Joseph Higg, Yuanzhe Bian, Chae Won Ryu, Jerry O. Talton, Ranjitha Kumar
Venue: WWW
Year: 2019

In the last two decades, Emoji have become a mainstay of digital communication, allowing ordinary people to convey ideas, concepts, and emotions with just a few Unicode characters. While emoji are most often used to supplement text in digital communication, they comprise a powerful and expressive vocabulary in their own right. In this paper, we study the affordances of “emoji-first” communication, in which sequences of emoji are used to describe concepts without textual accompaniment. To investigate the properties of emoji-first communication, we built and released Opico, a social media mobile app that allows users to create reactions — sequences of between one and five emoji — and share them with a network of friends. We then leveraged Opico to collect a repository of more than 3700 emoji reactions from more than 1000 registered users, each tied to one of 2441 physical places. We describe the design and architecture of the Opico app, present a qualitative and quantitative analysis of Opico’s reaction dataset, and discuss the implications of Emoji-first communication for future social platforms.
From Small-scale to Large-scale Text Classification
Authors: Kang-Min Kim, Yeachan Kim, Jungho Lee, Ji-Min Lee, SangKeun Lee
Venue: WWW
Year: 2019

Neural network models have achieved impressive results in the field of text classification. However, existing approaches often suffer from insufficient training data in a large-scale text classification involving a large number of categories (e.g., several thousands of categories). Several neural network models have utilized multi-task learning to overcome the limited amount of training data. However, these approaches are also limited to small-scale text classification. In this paper, we propose a novel neural network-based multi-task learning framework for large-scale text classification. To this end, we first treat the different scales of text classification (i.e., large and small numbers of categories) as multiple, related tasks. Then, we train the proposed neural network, which learns small- and large-scale text classification tasks simultaneously. In particular, we further enhance this multi-task learning architecture by using a gate mechanism, which controls the flow of features between the small- and large-scale text classification tasks. Experimental results clearly show that our proposed model improves the performance of the large-scale text classification task with the help of the small-scale text classification task. The proposed scheme exhibits significant improvements of as much as 14% and 5% in terms of micro-averaging and macro-averaging F1-score, respectively, over state-of-the-art techniques.
Humans, Machines and Disagreement
Authors: Brad Klingenberg
Venue: WWW
Year: 2019

Most recommendation algorithms produce results without humans-in-the-loop. Combining algorithms with expert human curation can make recommendations much more effective, especially in hard-to-quantify domains like fashion. But it also makes things more complicated, introducing new sources of statistical bias and challenges for traditional approaches to training and evaluating algorithms. Humans and machines can also disagree, further complicating the design of production systems. In this talk I'll share lessons from combining algorithms and human judgement for personal styling recommendations at Stitch Fix, an online personal styling service that commits committed to its recommendations through the physical delivery of merchandise to clients.
Reputation Deflation Through Dynamic Expertise Assessment in Online Labor Markets
Authors: Marios Kokkodis
Venue: WWW
Year: 2019

Current reputation systems in online labor markets (e.g., Freelancer, PeoplePerHour) experience three major shortcomings: (1) reputation inflation (i.e., reputation scores are inflated to “above average” values) (2) reputation attribution (i.e., attribution of reputation scores to individual skills is unfeasible) and (3) reputation staticity (i.e., reputation scores are uniformly averaged over time). These shortcomings render online reputation systems uninformative, and sometimes even misleading. This work proposes a data-driven approach that deflates reputation scores by solving the problems of reputation attribution and saticity. The deflating process starts with projecting any random set of skills to a set of competency dimensions. For each competency dimension, a Hidden Markov Model estimates a contractor's current (but latent) competency-specific expertise. Aggregation of competency-specific estimates provides expertise predictions for any given set of required skills. Empirical analysis on 61,330 completed tasks from a major online labor market shows that the resulting estimates are deflated and they better predict contractor performance. These results suggest a series of implications for online (labor) markets and their users.
Mobile App Risk Ranking via Exclusive Sparse Coding
Authors: Deguang Kong, Lei Cen
Venue: WWW
Year: 2019

To improve mobile application (App for short) user experience, it is very important to inform the users about the apps' privacy risk levels. To address the challenge of incorporating the heterogeneous feature indicators (such as app permissions, user review, developers' description and ads library) into the risk ranking model, we formalize the app risk ranking problem as an exclusive sparse coding optimization problem by taking advantage of features from different modalities via the maximization of the feature consistency and enhancement of feature diversity. We propose an efficient iterative re-weighted method to solve the resultant optimization problem, the convergence of which can be rigorously proved. The extensive experiments demonstrate the consistent performance improvement using the real-world mobile application datasets (totally 13786 apps, 37966 descriptions, 10557681 user reviews and 200 ad libraries).
The Social Science of Privacy - Effects on Industry and Government Data Use
Authors: Frauke Kreuter
Venue: WWW
Year: 2019

The sharing of data is at the core of many technology companies. Data sharing is also increasingly important for government decision-making, as stated by the Commission on Evidenced-Based Policymaking, which led to the Foundations for Evidence-Based Policymaking Act. However, in many instances, data used for decision-making is generated by people, and needs to be explicitly shared by the data subject with those wanting to use the data. The decision-making process behind sharing (private) information needs to be understood to assess (and circumvent) potential biases in the resulting data. When assessing bias in algorithmic decision-making, awareness of biases in the training data is essential. This presentation will review social science theories behind data sharing decision-making, highlight a series of experimental studies designed to affect sharing decisions, and present a framework design to detect sources of bias in various data sources.
Digital Companion for Industry
Authors: Mareike Kritzler, Jack Hodges, Dan Yu, Kimberly García, Hemant Shukla, Florian Michahelles
Venue: WWW
Year: 2019

The industrial domain offers a high degree of standardization, a variety of very specialized use cases, and an abundance of resources. These characteristics provide perfect conditions for Digital Companion systems. A Digital Companion is a cognitive agent that assists human users by taking on three roles: as guardians, assistants or mentors, and partners. This paper describes the characteristics, conceptual architecture, use cases and open challenges regarding Digital Companions for industry.
ProductQnA: Answering User Questions on E-Commerce Product Pages
Authors: Ashish Kulkarni, Kartik Mehta, Shweta Garg, Vidit Bansal, Nikhil Rasiwasia, Srinivasan H. Sengamedu
Venue: WWW
Year: 2019

Product pages on e-commerce websites often overwhelm their customers with a wealth of data, making discovery of relevant information a challenge. Motivated by this, here, we present a novel framework to answer both factoid and non-factoid user questions on product pages. We propose several question-answer matching models leveraging both deep learned distributional semantics and semantics imposed by a structured resource like a domain specific ontology. The proposed framework supports the use of a combination of these models and we show, through empirical evaluation, that a cascade of these models does much better in meeting the high precision requirements of such a question-answering system. Evaluation on user asked questions shows that the proposed system achieves 66% higher precision1 as compared to IDF-weighted average of word vectors baseline [1].
The Power of Temporal Features for Classifying News Articles
Authors: Lukas Lange, Omar Alonso, Jannik Strötgen
Venue: WWW
Year: 2019

Temporal information extracted from texts and normalized to some standard format has been exploited in a variety of tasks such as information retrieval and question answering. Classifying documents into categories using temporal features has not yet been tried. Such a method might be particularly valuable when classifying sensitive texts such as patient records, i.e., whenever the pure content of the documents should not be used for the classification. In this paper, we describe, as a proof-of-concept, our work on classifying news articles exploiting only features defined over extracted and normalized temporal expressions. Our evaluation of two classification models on large German and English news archives shows promising results and demonstrates the discriminative power of temporal features for topically classifying text documents.
Hanmoji: What Chinese Characters and Emoji Reveal About Each Other
Authors: Jennifer Lee, Jason Li, An Xiao Mina
Venue: WWW
Year: 2019

Chinese characters predate the introduction of digital emoji by approximately 3,000 years. Despite the temporal gap, there are striking parallels between the canonical set of Chinese radicals and the set of emoji that have currently been approved by the Unicode Consortium. Comparing the 214 Kangxi Chinese radicals with the 3,019 emojis in the Unicode 12.0 set can reveal semantic gaps and provide directions for new emoji. Our analysis found that 72.4% of radicals have reasonable emoji equivalents, while only 17.8% of radicals lack any emoji equivalent that we could determine.
Discovering User Bias in Ordinal Voting Systems
Authors: Alyssa Lees, Chris Welty
Venue: WWW
Year: 2019

Crowdsourcing systems increasingly rely on users to provide more subjective ground truth for intelligent systems - e.g. ratings, aspect of quality and perspectives on how expensive or lively a place feels, etc. We focus on the ubiquitous implementation of online user ordinal voting (e.g 1-5, 1 star-4 stars) on some aspect of an entity, to extract a relative truth, measured by a selected metric such as vote plurality or mean. We argue that this methodology can aggregate results that yield little information to the end user. In particular, ordinal user rankings often converge to a indistinguishable rating. This is demonstrated by the trend in certain cities for the majority of restaurants to all have a 4 star rating. Similarly, the rating of an establishment can be significantly affected by a few users [10]. User bias in voting is not spam, but rather a preference that can be harnessed to provide more information to users. We explore notions of both global skew and user bias. Leveraging these bias and preference concepts, the paper suggests explicit models for better personalization and more informative ratings.
The Law of the Horse at 20: Phases of the Net
Authors: Lawrence Lessig
Venue: WWW
Year: 2019

The Internet moves in phases, and we are entering the third in 20 years. In this keynote, using a framework drawn from the Law of the Horse [1], I describe the phase we are entering - the surveillance phase - and the threat it presents to society generally, and democracy in particular. Along the way, I offer an understanding of the Net circa 1999, and the phase that followed it, circa 2009. At each stage, our inability to govern has been a significant liability. In the phase we are entering, it will be devastating.
Exploiting Ratings, Reviews and Relationships for Item Recommendations in Topic Based Social Networks
Authors: Pengfei Li, Hua Lu, Gang Zheng, Qian Zheng, Long Yang, Gang Pan
Venue: WWW
Year: 2019

Many e-commerce platforms today allow users to give their rating scores and reviews on items as well as to establish social relationships with other users. As a result, such platforms accumulate heterogeneous data including numeric scores, short textual reviews, and social relationships. However, many recommender systems only consider historical user feedbacks in modeling user preferences. More specifically, most existing recommendation approaches only use rating scores but ignore reviews and social relationships in the user-generated data. In this paper, we propose TSNPF-a latent factor model to effectively capture user preferences and item features. Employing Poisson factorization, TSNPF fully exploits the wealth of information in rating scores, review text and social relationships altogether. It extracts topics of items and users from the review text and makes use of similarities between user pairs with social relationships, which results in a comprehensive understanding of user preferences. Experimental results on real-world datasets demonstrate that our TSNPF approach is highly effective at recommending items to users.