OpenRaiser/Intern-Atlas huggingface dataset:
Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces
Authors: Longqi Yang, Chen Fang, Hailin Jin, Matthew D. Hoffman, Deborah Estrin
Venue: WWW
Year: 2017

Users of software applications generate vast amounts of unstructured log-trace data. These traces contain clues to the intentions and interests of those users, but service providers may find it difficult to uncover and exploit those clues. In this paper, we propose a framework for personalizing software and web services by leveraging such unstructured traces. We use 6 months of Photoshop usage history and 7 years of interaction records from 67K Behance users to design, develop, and validate a user-modeling technique that discovers highly discriminative representations of Photoshop users; we refer to the model as cutilization-to-vector, util2vec. We demonstrate the promise of this approach for three sample applications: (1) a practical user-tagging system that automatically predicts areas of focus for millions of Photoshop users; (2) a two-phase recommendation model that enables cold-start personalized recommendations for many new Behance users who have Photoshop usage data, improving recommendation quality ([email protected]) by 21.2% over a popularity-based recommender; and (3) a novel inspiration engine that provides real-time personalized inspirations to artists. We believe that this work demonstrates the potential impact of unstructured usage-log data for personalization.
The Werther Effect Revisited: Measuring the Effect of News Items on User Behavior
Authors: Elad Yom-Tov, Shira H. Fischer
Venue: WWW
Year: 2017

People are moved to act following exposure to media coverage of specific events. For example, the ``Werther Effect" is the popular term for the observed increase in suicides following media coverage of suicides. Here we develop a fine-grained method for assessing the effect of news stories on the intentions of internet users. Our method assesses the likelihood that a person was exposed to a given news story via the temporal and spatial distances between the location of the person and the location of the news story and/or the website where it was published. This analysis of likelihoods allows us to estimate the contribution of a particular news story to a person's intent, as manifested in specific, intent-driven, search engine queries.
Reducing Latency by Eliminating Synchrony
Authors: Min Hong Yun, Songtao He, Lin Zhong
Venue: WWW
Year: 2017

Drawing or dragging an object on a mobile device is annoying today because the latency is manifested spatially with an obvious gap between the touch point and the line head or dragged object. This work identifies the multiple synchronization points in the input to display path of modern mobile systems as a major source of latency, contributing about 30 ms to the overall latency.
Caching at the Web Scale: [Tutorial]
Authors: Victor Zakhary, Divyakant Agrawal, Amr El Abbadi
Venue: WWW
Year: 2017

Today's web applications and social networks are serving billions of users around the globe. These users generate billions of key lookups and millions of data object updates per second. A single user's social network page load requires hundreds of key lookups. This scale creates many design challenges for the underlying storage systems. First, these systems have to serve user requests with low latency. Any increase in the request latency leads to a decrease in user interest. Second, storage systems have to be highly available. Failures should be handled seamlessly without affecting user requests. Third, users consume an order of magnitude more data than they produce. Therefore, storage systems have to be optimized for read-intensive workloads. To address these challenges, distributed in-memory caching services have been widely deployed on top of persistent storage. In this tutorial, we survey the recent developments in distributed caching services. We present the algorithmic and architectural efforts behind these systems focusing on the challenges in addition to open research questions.
Quantifying Structural Patterns of Information Cascades
Authors: Chengxi Zang, Peng Cui, Chaoming Song, Christos Faloutsos, Wenwu Zhu
Venue: WWW
Year: 2017

Information cascades are ubiquitous in both physical society and online social media, taking on large variations in structures, dynamics and semantics. Although there has been much progress on understanding the dynamics and semantics of information cascades, little is known about their structural patterns. In this paper, we explore a large-scale dataset including 432 million information cascades with explicit records of spreading traces. We find that the structural complexity of information cascades is far beyond the previous conjectures. We first propose seven-dimensional metrics, which reflect size and spreading orientation aspects, to quantify the structural characteristics of millions of information cascades. Further, we analyze the correlations of these metrics, finding some brand new structure patterns of information cascades, potentially providing insights into intrinsic mechanisms governing information spreading in nature and new models to forecast as well as to impose good control over information cascades in real applications.
Item Group Recommendation: A Method Based on Game Theory
Authors: Limeng Zhang, Rui Zhou, Haixin Jiang, Hua Wang, Yanchun Zhang
Venue: WWW
Year: 2017

In this paper, we focus on recommending an item set to multiple users. Group recommender systems are designed to deal with the issue of recommending items for a user group. However, in some scenarios such as gift set promotion (different items are packed together as a gift set), album promotion, we need to focus on consumers' preference to multiple items rather than to some specific item. To deal with this issue, we pioneer a Nash equilibrium based Item Group Recommendation approach (NIGR). Specifically, we evaluate each consumer's preference to an item group in two perspectives, interest part from the customer herself and social affection from her friends. Then, we model the recommending process as a game to achieve Nash equilibrium. Finally, we demonstrate the effectiveness of our approach with extensive experiments.
On the Primitivity of SPARQL 1.1 Operators
Authors: Xiaowang Zhang
Venue: WWW
Year: 2017

The paper studies the primitivity of the eleven basic operators used in the SPARQL 1.1 query language. This paper shows that the six operators BIND, FILTER, GRAPH, property path, SELECT, and VALUES are primitive while the left five operators AND, EXISTS, MINUS, OPT, and UNION, are not primitive. It is surprised that OPT and UNION which are primitive in SPARQL 1.0 become no longer primitive in SPARQL 1.1.
Smart Jump: Automated Navigation Suggestion for Videos in MOOCs
Authors: Han Zhang
Venue: WWW
Year: 2017

Statistics show that, on average, each user of Massive Open Online Courses (MOOCs) uses "jump-back" to navigate a course video for 2.6 times. In this work, employing one of the largest Chinese MOOCs, XuetangX.com, as the source for our research, we study the extent to which we can develop a methodology to understand the user intention and help the user alleviate this problem by suggesting the best position for a jump-back. We demonstrate that it is possible to accurately predict 90% of users' jump-back intentions in the real online system. Moreover, our study reveals several interesting patterns, e.g., students in non-science courses tend to jump back from the first half of the course video, and students in science courses tend to replay for longer time.
Alienated Digital Identities
Authors: Rongxin Zhang
Venue: WWW
Year: 2017

Digital identities allow friends to like us, governments to analyze us and media platforms to monetize us. As the internet has evolved, so has the creation, storage and access to digital identities. This paper presents the notion that modern digital identities are no longer natural extensions of the user, but alienated entities that exist outside of their control. To justify this claim, this research analyses the externalized storage of digital identities and how this lessens the user's inherent rights and access to them. It introduces concepts such as 'derivative' digital identities and how the over-valuation of company stocks contributes to this alienation. The goal of the research is to justify the need for users to reclaim the ownership, storage, and access to their digital identities. This paper proposes a technical guideline and discusses the benefits and challenges of developing such a system.
Intelligent RDD Management for High Performance In-Memory Computing in Spark
Authors: Mingyue Zhang, Renhai Chen, Xiaowang Zhang, Zhiyong Feng, Guozheng Rao, Xin Wang
Venue: WWW
Year: 2017

Spark is a pervasively used in-memory computing framework in the era of big data, and can greatly accelerate the computation speed by wrapping the accessed data as resilient distribution datasets (RDDs) and storing these datasets in the fast accessed main memory. However, the space of main memory is limited, and Spark does not provide an intelligent mechanism to store reasonable RDDs in the limited memory. In this paper, we propose a fine-grained RDD checkpointing and kick-out selection strategy, by which Spark can intelligently select the reasonable RDDs to maximize the memory usage. The experiment is conducted on a server with four nodes. Experimental results demonstrate that the proposed techniques can effectively accelerate the execution speed.
Local Low-Rank Matrix Approximation with Preference Selection of Anchor Points
Authors: Menghao Zhang, Binbin Hu, Chuan Shi, Bai Wang
Venue: WWW
Year: 2017

Matrix factorization is widely used in personalized recommender systems, text mining, and computer vision. A general assumption to construct matrix approximation is that the original matrix is of global low rank, while Joonseok Lee et al. proposed that many real matrices may be not globally low rank, and thus a locally low-rank matrix approximation method has been proposed.[11] However, this kind of matrix approximation method still leaves some important issues unsolved, for example, the randomly selecting anchor nodes. In this paper, we study the problem of the selection of anchor nodes to enhance locally low-rank matrix approximation. We propose a new model for local low-rank matrix approximation which selects anchor-points using a heuristic method. Our experiments indicate that the proposed method outperforms many state-of-the-art recommendation methods. Moreover, the proposed method can significantly improve algorithm efficiency, and it is easy to parallelize. These traits make it potential for large scale real-world recommender systems.
Inferring the Student Social Loafing State in Collaborative Learning with a Hidden Markov Model: A Case on Slack
Authors: Xi Zhang, Shan Jiang, Yihang Cheng
Venue: WWW
Year: 2017

With the increasingly prevailing usage of Information and Communication technologies (ICT) in collaborative learning, students can cooperate with others online easily, in spite of the restriction of time and location. Social loafing, a common phenomenon in collaborative work, has negative effect on team performance, especially on the individual's knowledge sharing behavior. In recent years, there are also some researches pointing out that social loafing is a kind of hidden and unobservable behavior. In this study, we propose a research model based on the stimulus-organism-response (S-O-R) framework and build a hidden Markov model (HMM) to infer the student's unobservable social loafing state. We collect real world behavior data from an online collaborative course from Nov 11th 2016 to Dec 21th 2016.The dataset includes more than 1200 knowledge sharing records from 150 students on Slack. Our research is expected to contribute in both academic study and managerial implications on how to set up a collaborative team.
Is Tofu the Cheese of Asia?: Searching for Corresponding Objects across Geographical Areas
Authors: Yating Zhang, Adam Jatowt, Katsumi Tanaka
Venue: WWW
Year: 2017

Keyword-based search engines are widely used nowadays for content retrieval. Creating queries is relatively easy when users wish to retrieve content in familiar domains (e.g., information about things within their own country). However, they often struggle when searching in unfamiliar domains (e.g., searching for information related to a foreign country). In this paper, we approach the vocabulary gap problem by allowing users to search by analogical examples, that is, by letting them utilize information in familiar domains to perform search in domains unfamiliar to them. In particular, we focus on geographical domains. We propose to build connections between two different spaces (e.g., USA and Japan) by mapping the distributed word representations in one space with the ones in the other space. We first introduce an effective technique for automatically constructing seed pairs of terms to be used for finding the optimal mapping function. Then we propose general and topic-based transformations of terms from one space to another. We test the performance of the proposed approaches on datasets derived from Wikipedia which are related to two quite diverse countries: Japan and USA.
Top-K Entity Units Retrieval Over Big Data
Authors: Da Zhang, Mansur R. Kabuka
Venue: WWW
Year: 2017

During the past several years, data size has increased explosively. This data explosion tendency has impacted various fields ranging from biomedical engineering, business consulting to social media and mobile application. Big Data is a two sided sword. While it provides incredibly treasured insights in commercial scope and innovative discovery in the scientific field, Big Data also has many challenges, such as complication in data storage, data processing, data analysis and data visualization. Among all these challenges, keyword searching over a large volume of data prevails as one of the four tasks defined by Bizer et al. at the year of 2012. Keyword searching refers to retrieving the objects relevant to the entities of concern using scientific computational methods. Consequently, efficiently solving the problem of keyword searching can contribute as a foundation to diverse Big Data applications.
Navigation Configuration and Placement Influences the Visual Search Efficiency and Preference
Authors: Fan Zhang, Siyuan Lin, Xuan Li, Yingbin Shuai, Hao Jiang, Cheng Yao, Fangtian Ying, Feng Gao
Venue: WWW
Year: 2017

As the basic and ubiquitous elements of interfaces, navigation is used to search target information items. Many prior research has shown that how does the navigation placement influence users' visual search efficiency and preference, and owing to the format of navigation is hierarchical navigation in most of e-commerce websites today. So designers need to consider about the navigation configuration. To investigate the effects of navigation design on usersv experience in e-commerce websites, we performed an experimental study to better understand what kind of two-level hierarchical menu layout users preferred, and two factors we controlled were configuration and placement. These data indicate that the manual placement could significantly influence users' preference. And the influence of configuration is significant with navigation placed on the top or left.
A Study of How List Format Influences the Visual Search Performance
Authors: Fan Zhang, Yingbin Shuai, Siyuan Lin, Xuan Li, Hao Jiang, Cheng Yao, Fangtian Ying, Minneng Lin
Venue: WWW
Year: 2017

Previous research has shown that format design has an impact on the usability of listing pages. This study investigated the effects of specific list presentation format on visual search performance and subjective satisfaction in e-commerce listing pages. At first, we found seven important commodity features for consumers through pre-study. Then, an eye tracking study was conducted to record the visual search for target items and cognitive workload based on three different list formats (Vertical Format/T Format/Block format) in e-commerce websites. The results suggested that list format could significantly influence the visual search performance and satisfaction. The efficiency of Vertical format and T format is higher than block format. Designers could get some valid references from this result when they are designing listing pages.
Smart Jump: Automated Navigation Suggestion for Videos in MOOCs
Authors: Han Zhang, Maosong Sun, Xiaochen Wang, Zhengyang Song, Jie Tang, Jimeng Sun
Venue: WWW
Year: 2017

Statistics show that, on average, each user of Massive Open Online Courses (MOOCs) uses 'jump-back' to navigate a course video for 2.6 times. By taking a closer look at the navigation data, we found that more than half of the jump-backs are due to the 'bad' positions of the previous jump-backs. In this work, employing one of the largest Chinese MOOCs, XuetangX.com, as the source for our research, we study the extent to which we can develop a methodology to understand the user intention and help the user alleviate this problem by suggesting the best position for a jumpback. We demonstrate that it is possible to accurately predict 90% of users' jump-back intentions in the real online system. Moreover, our study reveals several interesting patterns, e.g., students in nonscience courses tend to jump back from the first half of the course video, and students in science courses tend to replay for longer time.
Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning
Authors: Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, Jiawei Han
Venue: WWW
Year: 2017

With the ever-increasing urbanization process, systematically modeling people's activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been fruitful studies on discovering geographical topics from GTSM data. However, their high computational costs and strong distributional assumptions about the latent topics hinder them from fully unleashing the power of GTSM. To bridge the gap, we present CrossMap, a novel cross-modal representation learning method that uncovers urban dynamics with massive GTSM data. CrossMap first employs an accelerated mode seeking procedure to detect spatiotemporal hotspots underlying people's activities. Those detected hotspots not only address spatiotemporal variations, but also largely alleviate the sparsity of the GTSM data. With the detected hotspots, CrossMap then jointly embeds all spatial, temporal, and textual units into the same space using two different strategies: one is reconstruction-based and the other is graph-based. Both strategies capture the correlations among the units by encoding their co-occurrence and neighborhood relationships, and learn low-dimensional representations to preserve such correlations. Our experiments demonstrate that CrossMap not only significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.
Sequential Transfer Learning: Cross-domain Novelty Seeking Trait Mining for Recommendation
Authors: Fuzhen Zhuang, Yingmin Zhou, Fuzheng Zhang, Xiang Ao, Xing Xie, Qing He
Venue: WWW
Year: 2017

Recent studies in psychology suggest that novelty-seeking trait is highly related to consumer behavior, which has a profound business impact on online recommendation. This paper studies the problem of mining novelty seeking trait across domains to improve the recommendation performance in target domain. We propose an efficient model, CDNST, which significantly improves the recommendation performance by transferring the knowledge from auxiliary source domain. We conduct extensive experiments on three domain datasets crawled from Douban (www.douban.com) to demonstrate the effectiveness of the proposed model. Moreover, we find that the property of sequential data affects the performance of CDNST.
Does Weather Matter?: Causal Analysis of TV Logs
Authors: Shi Zong, Branislav Kveton, Shlomo Berkovsky, Azin Ashkan, Nikos Vlassis, Zheng Wen
Venue: WWW
Year: 2017

Weather affects our mood and behaviors, and many aspects of our life. When it is sunny, most people become happier; but when it rains, some people get depressed. Despite this evidence and the abundance of data, weather has mostly been overlooked in the machine learning and data science research. This work presents a causal analysis of how weather affects TV watching patterns. We show that some weather attributes, such as pressure and precipitation, cause major changes in TV watching patterns. To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.
SMARTGEN: Exposing Server URLs of Mobile Apps With Selective Symbolic Execution
Authors: Chaoshun Zuo, Zhiqiang Lin
Venue: WWW
Year: 2017

Server URLs including domain names, resource path, and query parameters are important to many security applications such as hidden service identification, malicious website detection, and server vulnerability fuzzing. Unlike traditional desktop web apps in which server URLs are often directly visible, the server URLs of mobile apps are often hidden, only being exposed when the corresponding app code gets executed. Therefore, it is important to automatically analyze the mobile app code to expose the server URLs and enable the security applications with them. We have thus developed SMARTGEN to feature selective symbolic execution for the purpose of automatically generate server request messages to expose the server URLs by extracting and solving user input constraints in mobile apps. Our evaluation with 5,000 top-ranked mobile apps (each with over one million installs) in Google Play shows that with SMARTGEN we are able to reveal 297,780 URLs in total for these apps. We have then submitted all of these exposed URLs to a harmful URL detection service provided by VirusTotal, which further identified 8634 URLs being harmful. Among them, Phising belong to phishing sites, 3,722 malware sites and 3,228 malicious sites (there are 387 overlapped sites between malware and malicious sites).