OpenRaiser/Intern-Atlas huggingface dataset:
Better Caching in Search Advertising Systems with Rapid Refresh Predictions
Authors: Conglong Li, David G. Andersen, Qiang Fu, Sameh Elnikety, Yuxiong He
Venue: WWW
Year: 2018

To maximize profit and connect users to relevant products and services, search advertising systems use sophisticated machine learning algorithms to estimate the revenue expectations of thousands of matching ad listings per query. These machine learning computations constitute a substantial part of the operating cost, e.g., 10% to 30% of the total gross revenues. It is desirable to cache and reuse previous computation results to reduce this cost, but caching introduces approximation which comes with potential revenue loss. To maximize cost savings while minimizing the overall revenue impact, an intelligent refresh policy is required to decide when to refresh the cached computation results. The state-of-the-art manually-tuned refresh heuristic uses revenue history to assign different refresh frequencies. Using the gradient boosting regression tree algorithm with well selected features, we introduce a rapid prediction framework that provides refresh decisions at higher accuracy compared to the heuristic. This enables us to build a prediction-based refresh policy and a cache achieving higher profit without manual parameter tuning. Simulations conducted on the logs from a major commercial search advertising system show that our proposed cache design reduces the negative revenue impact (0.07x), and improves the cost savings (1.41x) and the net profit (1.50~1.70x) compared to the state-of-the-art manually-tuned heuristic-based cache design.
AdaError: An Adaptive Learning Rate Method for Matrix Approximation-based Collaborative Filtering
Authors: Dongsheng Li, Chao Chen, Qin Lv, Hansu Gu, Tun Lu, Li Shang, Ning Gu, Stephen M. Chu
Venue: WWW
Year: 2018

Gradient-based learning methods such as stochastic gradient descent are widely used in matrix approximation-based collaborative filtering algorithms to train recommendation models based on observed user-item ratings. One major difficulty in existing gradient-based learning methods is determining proper learning rates, since model convergence would be inaccurate or very slow if the learning rate is too large or too small, respectively. This paper proposes AdaError, an adaptive learning rate method for matrix approximation-based collaborative filtering. AdaError eliminates the need of manually tuning the learning rates by adaptively adjusting the learning rates based on the noisiness level of user-item ratings, using smaller learning rates for noisy ratings so as to reduce their impact on the learned models. Our theoretical and empirical analysis shows that AdaError can improve the generalization performance of the learned models. Experimental studies on the MovieLens and Netflix datasets also demonstrate that AdaError outperforms state-of-the-art adaptive learning rate methods in matrix approximation-based collaborative filtering. Furthermore, by applying AdaError to the standard matrix approximation method, we can achieve statistically significant improvements over state-of-the-art collaborative filtering methods in both rating prediction accuracy and top-N recommendation accuracy.
SocialNLP@WWW 2018 Chairs' Welcome & Organization
Authors: Cheng-Te Li, Lun-Wei Ku
Venue: WWW
Year: 2018

With the rapid growing of social networking services (e.g., Facebook and Twitter), being able to process data come from such platforms has gained much attention in recent years. SocialNLP is a new inter-disciplinary area of natural language processing (NLP) and social computing. There are three plausible directions of SocialNLP: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social media; and (3) handling new problems related to both social computing and natural language processing. Several challenges are foreseeable in SocialNLP. First, the message lengths on social media are usu-ally short, and thus it is difficult to apply traditional NLP approaches directly. Second, social media contains heterogeneous information (e.g. tags, friends, followers, likes, and retweets) that should be considered together with the contents for better quality of analysis. Finally, social media contents always involve multiple persons with slangs and jargons, and usually require special techniques to process. We organize SocialNLP in WWW 2018 with three goals. First, social media data is essentially generated and collected from online social services that are functioned based on Web techniques. One can leverage Web techniques to investigate various user behaviors and investigate the interactions between users. Second, user-generated data in social media is mainly in the form of text. Theories and techniques on Web information retrieval and natural language processing are desired for semantic understanding, accurate search, and efficient processing of big social media data. Third, from the perspective of application, if social media data can be effectively processed to distill the collective knowledge of users, novel Web applications, such as emergency management, social recommendation, and future prediction, can be developed with higher accuracy and better user experience. We expect SocialNLP workshop in WWW community can provide mutually-reinforced benefits for researchers in areas of Web techniques, information retrieval and social media analytics.
A Story Coherence based Neural Network Model for Predicting Story Ending
Authors: Qian Li, Ziwei Li, Jin-Mao Wei, Zhenglu Yang, Yanhui Gu, R. Uday Kiran
Venue: WWW
Year: 2018

Predicting the ending of a story is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). Although several studies have addressed this issue, the performance remains unsatisfactory due to ineffectiveness of story comprehension. In this paper, we propose to construct a story coherence based neural network model (SCNN) with well-designed optimizations. The preliminary evaluation demonstrates the effectiveness of our model which is superior to that of state-of-the-art approaches.
Understanding and Predicting Delay in Reciprocal Relations
Authors: Jundong Li, Jiliang Tang, Yilin Wang, Yali Wan, Yi Chang, Huan Liu
Venue: WWW
Year: 2018

Reciprocity in directed networks points to user»s willingness to return favors in building mutual interactions. High reciprocity has been widely observed in many directed social media networks such as following relations in Twitter and Tumblr. Therefore, reciprocal relations between users are often regarded as a basic mechanism to create stable social ties and play a crucial role in the formation and evolution of networks. Each reciprocity relation is formed by two parasocial links in a back-and-forth manner with a time delay. Hence, understanding the delay can help us gain better insights into the underlying mechanisms of network dynamics. Meanwhile, the accurate prediction of delay has practical implications in advancing a variety of real-world applications such as friend recommendation and marketing campaign. For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing. This paper presents the initial investigation of the time delay in reciprocal relations. Our study is based on a large-scale directed network from Tumblr that consists of 62.8 million users and 3.1 billion user following relations with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal a number of interesting patterns about the delay that motivate the development of a principled learning model to predict the delay in reciprocal relations. Experimental results on the above mentioned dynamic networks corroborate the effectiveness of the proposed delay prediction model.
Human-Guided Flood Mapping: From Experts to the Crowd
Authors: Jiongqian Liang, Peter Jacobs, Srinivasan Parthasarathy
Venue: WWW
Year: 2018

Hurricane-induced flooding can lead to substantial loss of life and huge damage to infrastructure. Mapping flood extent from satellite or aerial imagery is essential for prioritizing relief efforts and for assessing future flood risk. Identification of water extent in such images can be challenging considering the heterogeneity in water body size and shape, cloud cover, and natural variations in land cover. In this effort, we introduce a novel cognitive framework based on a semi-supervised learning algorithm, called HUman-Guided Flood Mapping (HUG-FM), specifically designed to tackle the flood mapping problem. Our framework first divides the satellite or aerial image into patches leveraging a graph-based clustering approach. A domain expert is then asked to provide labels for a few patches (as opposed to pixels which are harder to discern). Subsequently, we learn a classifier based on the provided labels to map flood extent. We test the efficacy and efficiency of our framework on imagery from several recent flood-induced emergencies and results show that our algorithm can robustly and correctly detect water areas compared to the state-of-the-art. We then evaluate whether expert guidance can be replaced by the wisdom of a crowd (e.g., crisis volunteers). We design an online crowdsourcing platform based on HUG-FM and propose a novel ensemble method to leverage crowdsourcing efforts. We conduct an experiment with over $50$ participants and show that crowdsourced HUG-FM (CHUG-FM) can approach or even exceed the performance of a single expert providing guidance (HUG-FM).
AttAE-RL²: Attention based Autoencoder for Rap Lyrics Representation Learning
Authors: Hongru Liang, Qian Li, Haozheng Wang, Hang Li, Jin-Mao Wei, Zhenglu Yang
Venue: WWW
Year: 2018

Learning rap lyrics is an important area of music information retrieval because it is the basis of many applications, such as recommendation systems, automatic classification. In this paper, we tackle the issue pertaining to the lack of an effective approach to aggregate various features of lyrics by proposing an attention-based autoencoder for rap lyrics representation learning (AttAE-RL²). The proposed method appropriately integrates the semantic and prosodic features of rap lyrics. The preliminary experimental results demonstrate that our approach outperforms the state-of-the-art ones.
Manifold Learning for Rank Aggregation
Authors: Shangsong Liang, Ilya Markov, Zhaochun Ren, Maarten de Rijke
Venue: WWW
Year: 2018

We address the task of fusing ranked lists of documents that are retrieved in response to a query. Past work on this task of rank aggregation often assumes that documents in the lists being fused are independent and that only the documents that are ranked high in many lists are likely to be relevant to a given topic. We propose manifold learning aggregation approaches, ManX and v-ManX, that build on the cluster hypothesis and exploit inter-document similarity information. ManX regularizes document fusion scores, so that documents that appear to be similar within a manifold, receive similar scores, whereas v-ManX first generates virtual adversarial documents and then regularizes the fusion scores of both original and virtual adversarial documents. Since aggregation methods built on the cluster hypothesis are computationally expensive, we adopt an optimization method that uses the top-k documents as anchors and considerably reduces the computational complexity of manifold-based methods, resulting in two efficient aggregation approaches, a-ManX and a-v-ManX. We assess the proposed approaches experimentally and show that they significantly outperform the state-of-the-art aggregation approaches, while a-ManX and a-v-ManX run faster than ManX, v-ManX, respectively.
Tink: A Temporal Graph Analytics Library for Apache Flink
Authors: Wouter Ligtenberg, Yulong Pei, George H. L. Fletcher, Mykola Pechenizkiy
Venue: WWW
Year: 2018

We introduce the Tink library for distributed temporal graph analytics. Increasingly, reasoning about temporal aspects of graph-structured data collections is an important aspect of analytics. For example, in a communication network, time plays a fundamental role in the propagation of information within the network. Whereas existing tools for temporal graph analysis are built stand alone, Tink is a library in the Apache Flink ecosystem, thereby leveraging its advanced mature features such as distributed processing and query optimization. Furthermore, Flink requires little effort to process and clean the data without having to use different tools before analyzing the data. Tink focuses on interval graphs in which every edge is associated with a starting time and an ending time. The library provides facilities for temporal graph creation and maintenance, as well as standard temporal graph measures and algorithms. Furthermore, the library is designed for ease of use and extensibility.
The Grass is Greener on the Other Side: Understanding the Effects of Green Spaces on Twitter User Sentiments
Authors: Kwan Hui Lim, Kate E. Lee, Dave Kendal, Lida Rashidi, Elham Naghizade, Stephan Winter, Maria Vasardani
Venue: WWW
Year: 2018

Green spaces are believed to improve the well-being of users in urban areas. While there are urban research exploring the emotional benefits of green spaces, these works are based on user surveys and case studies, which are typically small in scale, intrusive, time-intensive and costly. In contrast to earlier works, we utilize a non-intrusive methodology to understand green space effects at large-scale and in greater detail, via digital traces left by Twitter users. Using this methodology, we perform an empirical study on the effects of green spaces on user sentiments and emotions in Melbourne, Australia and our main findings are: (i) tweets in green spaces evoke more positive and less negative emotions, compared to those in urban areas; (ii) each season affects various emotion types differently; (iii) there are interesting changes in sentiments based on the hour, day and month that a tweet was posted; and (iv) negative sentiments are typically associated with large transport infrastructures such as train interchanges, major road junctions and railway tracks. The novelty of our study is the combination of psychological theory, alongside data collection and analysis techniques on a large-scale Twitter dataset, which overcomes the limitations of traditional methods in urban research.
Online Compact Convexified Factorization Machine
Authors: Xiao Lin, Wenpeng Zhang, Min Zhang, Wenwu Zhu, Jian Pei, Peilin Zhao, Junzhou Huang
Venue: WWW
Year: 2018

Factorization Machine (FM) is a supervised learning approach with a powerful capability of feature engineering. It yields state-of-the-art performances in various batch learning tasks where all the training data is made available prior to the training. However, in real-world applications where the data arrives sequentially in a streaming manner, the high cost of re-training with batch learning algorithms has posed formidable challenges in the online learning scenario. The initial challenge is that no prior formulations of FM could directly fulfill the requirements in Online Convex Optimization (OCO) -- the paramount framework for online learning algorithm design. To address this aforementioned challenge, we invent a new convexification scheme leading to a Compact Convexified FM (CCFM) that seamlessly meets the requirements in OCO. However for learning Compact Convexified FM (CCFM) in the online learning settings, most existing algorithms suffer from expensive projection operations. To address this subsequent challenge, we follow the general projection-free algorithmic framework of Online Conditional Gradient and propose an Online Compact Convex Factorization Machine (OCCFM) algorithm that eschews the projection operation with efficient linear optimization steps. In support of the proposed OCCFM in terms of its theoretical foundation, we prove that the developed algorithm achieves a sub-linear regret bound. To evaluate the empirical performance of OCCFM, we conduct extensive experiments on 6 real-world datasets for online regression and online classification tasks. The experimental results show that OCCFM outperforms the state-of-art online learning methods for FM.
VizByWiki: Mining Data Visualizations from the Web to Enrich News Articles
Authors: Allen Yilun Lin, Joshua Ford, Eytan Adar, Brent J. Hecht
Venue: WWW
Year: 2018

Data visualizations in news articles (e.g., maps, line graphs, bar charts) greatly enrich the content of news articles and result in well-established improvements to reader comprehension. However, existing systems that generate news data visualiza-tions either require substantial manual effort or are limited to very specific types of data visualizations, thereby greatly re-stricting the number of news articles that can be enhanced. To address this issue, we define a new problem: given a news ar-ticle, retrieve relevant visualizations that already exist on the web. We show that this problem is tractable through a new system, VizByWiki, that mines contextually relevant data visualizations from Wikimedia Commons, the central file reposi-tory for Wikipedia. Using a novel ground truth dataset, we show that VizByWiki can successfully augment as many as 48% of popular online news articles with news visualizations. We also demonstrate that VizByWiki can automatically rank visualizations according to their usefulness with reasonable accuracy (nDCG@5 of 0.82). To facilitate further advances on our "news visualization retrieval problem", we release our ground truth dataset and make our system and its source code publicly available.
Machine Learning for the Peer Assessment Credibility
Authors: Yingru Lin, Soyeon Caren Han, Byeong Ho Kang
Venue: WWW
Year: 2018

The peer assessment approach is considered to be one of the best solutions for scaling both assessment and peer learning to global classrooms, such as MOOCs. However, some academic staff hesitate to use a peer assessment approach for their classes due to concerns about its credibility and reliability. The focus of our research is to detect the credibility level of each assessment performed by students during peer assessment. We found three major scopes in assessing the credibility level of evaluations, 1) Informativity, 2) Accuracy, and 3) Consistency. We collect assessments, including comments and grades provided by students during the peer assessment process and then each feedback-and-grade pair is labeled with its credibility level by Mechanical Turk evaluators. We extract relevant features from each labeled assessment and use them to build a classifier that attempts to automatically assess its level of credibility in C5.0 Decision Tree classifier. The evaluation results show that the model can be used to automatically classify peer assessments as credible or non-credible, with accuracy in the range of 88%.
LinCa: A Page Loading Time Optimization Approach for Users Subject to Internet Access Restriction
Authors: Chen Ling, Lei Wang, Jun Lang, Qiufen Xia, Guoxuan Chang, Kun Wang, Peng Zhao
Venue: WWW
Year: 2018

Internet access restriction in various areas unceasingly poses inconvenience for users. Unblocked websites or web pages always accompany couple fate-to-fail links from blocked servers due to such restrictions, while users wait for a long time to see contents of these blocked/invalid links. Therefore, it is better to directly show timeout links to users for better experiences rather than making users excessively wait. In this paper, we present LinCa (Links Catcher), a novel approach that fully considers the Internet access restriction and reduces page loading time on client-side by parsing all HTTP requests, and intercepting all invalid links when a web navigation starts. Thus, we first create and maintain a Rule Base to store invalid links under given access restriction rules. We then update the Rule Base periodically to cover as many invalid links as possible and remove links that become valid. We finally demonstrate the effectiveness of LinCa through experiments by building and deploying a Chrome extension. Experimental results show that LinCa can reduce page loading time with average 28.12% of original page loading time for our data sets.
Transforming the JSON Output of SPARQL Queries for Linked Data Clients
Authors: Pasquale Lisena, Raphaël Troncy
Venue: WWW
Year: 2018

SPARQL endpoints are one possible access method to linked data. The results of SPARQL queries serialized in JSON are, however, not suitable to be directly used by web developers in end-user applications who often need to merge the values resulting from variable bindings. In this work, we propose a generic approach implemented in a JavaScript module that takes as input a JSON file describing both the SPARQL query and the shape of the expected output at the same time.
"Satisfaction with Failure" or "Unsatisfied Success": Investigating the Relationship between Search Success and User Satisfaction
Authors: Mengyang Liu, Yiqun Liu, Jiaxin Mao, Cheng Luo, Min Zhang, Shaoping Ma
Venue: WWW
Year: 2018

User satisfaction has been paid much attention to in recent Web search evaluation studies. Although satisfaction is often considered as an important symbol of search success, it doesn»t guarantee success in many cases, especially for complex search task scenarios. In this study, we investigate the differences between user satisfaction and search success, and try to adopt the findings to predict search success in complex search tasks. To achieve these research goals, we conduct a laboratory study in which search success and user satisfaction are annotated by domain expert assessors and search users, respectively. We find that both "Satisfaction with Failure" and "Unsatisfied Success" cases happen in these search tasks and together they account for as many as 40.3% of all search sessions. The factors (e.g. document readability and credibility) that lead to the inconsistency of search success and user satisfaction are also investigated and adopted to predict whether one search task is successful. Experimental results show that our proposed prediction method is effective in predicting search success.
Content Attention Model for Aspect Based Sentiment Analysis
Authors: Qiao Liu, Haibin Zhang, Yifu Zeng, Ziqi Huang, Zufeng Wu
Venue: WWW
Year: 2018

Aspect based sentiment classification is a crucial task for sentiment analysis. Recent advances in neural attention models demonstrate that they can be helpful in aspect based sentiment classification tasks, which can help identify the focus words in human. However, according to our empirical study, prevalent content attention mechanisms proposed for aspect based sentiment classification mostly focus on identifying the sentiment words or shifters, without considering the relevance of such words with respect to the given aspects in the sentence. Therefore, they are usually insufficient for dealing with multi-aspect sentences and the syntactically complex sentence structures. To solve this problem, we propose a novel content attention based aspect based sentiment classification model, with two attention enhancing mechanisms: sentence-level content attention mechanism is capable of capturing the important information about given aspects from a global perspective, whiles the context attention mechanism is responsible for simultaneously taking the order of the words and their correlations into account, by embedding them into a series of customized memories. Experimental results demonstrate that our model outperforms the state-of-the-art, in which the proposed mechanisms play a key role.