OpenRaiser/Intern-Atlas huggingface dataset:
Temporally Like-minded User Community Identification through Neural Embeddings
Authors: Hossein Fani, Ebrahim Bagheri, Weichang Du
Venue: CIKM
Year: 2017

We propose a neural embedding approach to identify temporally like-minded user communities, i.e., those communities of users who have similar temporal alignment in their topics of interest. Like-minded user communities in social networks are usually identified by either considering explicit structural connections between users (link analysis), users' topics of interest expressed in their posted contents (content analysis), or in tandem. In such communities, however, the users' rich temporal behavior towards topics of interest is overlooked. Only few recent research efforts consider the time dimension and define like-minded user communities as groups of users who share not only similar topical interests but also similar temporal behavior. Temporal like-minded user communities find application in areas such as recommender systems where relevant items are recommended to the users at the right time. In this paper, we tackle the problem of identifying temporally like-minded user communities by leveraging unsupervised feature learning (embeddings). Specifically, we learn a mapping from the user space to a low-dimensional vector space of features that incorporate both topics of interest and their temporal nature. We demonstrate the efficacy of our proposed approach on a Twitter dataset in the context of three applications: news recommendation, user prediction and community selection, where our work is able to outperform the state-of-the-art on important information retrieval metrics.
SummIt: A Tool for Extractive Summarization, Discovery and Analysis
Authors: Guy Feigenblat, Odellia Boni, Haggai Roitman, David Konopnicki
Venue: CIKM
Year: 2017

We propose to demonstrate SummIt -- a tool for extractive summarization, discovery and analysis. The main goal of SummIt is to provide consumable summaries that are driven by users' information intents. To this end, SummIt discovers and analyzes potential intents that can be used for summarization. Given an intent, SummIt generates a summary based on a novel unsupervised, query-focused, extractive, multi-document summarization approach. Using visualization aids, SummIt further allows to analyze a given summary and explore both its narrow and broader context.
Rapid Analysis of Network Connectivity
Authors: Scott Freitas, Hanghang Tong, Nan Cao, Yinglong Xia
Venue: CIKM
Year: 2017

This research focuses on accelerating the computational time of two base network algorithms (k-simple shortest paths and minimum spanning tree for a subset of nodes)---cornerstones behind a variety of network connectivity mining tasks---with the goal of rapidly finding networkpathways andtrees using a set of user-specific query nodes. To facilitate this process we utilize: (1) multi-threaded algorithm variations, (2) network re-use for subsequent queries and (3) a novel algorithm, Key Neighboring Vertices (KNV), to reduce the network search space. The proposed KNV algorithm serves a dual purpose: (a) to reduce the computation time for algorithmic analysis and (b) to identify key vertices in the network (\textit ). Empirical results indicate this combination of techniques significantly improves the baseline performance of both algorithms. We have also developed a web platform utilizing the proposed network algorithms to enable researchers and practitioners to both visualize and interact with their datasets (PathFinder: http://www.path-finder.io.
HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning
Authors: Tao-Yang Fu, Wang-Chien Lee, Zhen Lei
Venue: CIKM
Year: 2017

In this paper, we propose a novel representation learning framework, namely HIN2Vec, for heterogeneous information networks (HINs). The core of the proposed framework is a neural network model, also called HIN2Vec, designed to capture the rich semantics embedded in HINs by exploiting different types of relationships among nodes. Given a set of relationships specified in forms of meta-paths in an HIN, HIN2Vec carries out multiple prediction training tasks jointly based on a target set of relationships to learn latent vectors of nodes and meta-paths in the HIN. In addition to model design, several issues unique to HIN2Vec, including regularization of meta-path vectors, node type selection in negative sampling, and cycles in random walks, are examined. To validate our ideas, we learn latent vectors of nodes using four large-scale real HIN datasets, including Blogcatalog, Yelp, DBLP and U.S. Patents, and use them as features for multi-label node classification and link prediction applications on those networks. Empirical results show that HIN2Vec soundly outperforms the state-of-the-art representation learning models for network data, including DeepWalk, LINE, node2vec, PTE, HINE and ESim, by 6.6% to 23.8% of $micro$-$f_1$ in multi-label node classification and 5% to 70.8% of $MAP$ in link prediction.
Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks
Authors: Felan Carlo C. Garcia, Erees Queen B. Macabebe
Venue: CIKM
Year: 2017

Energy management presents one of the principal sustainability challenges within urban centers given that they account for 75% of the energy consumption worldwide. In the context of a smart city framework, the use of intelligent urban systems provides a key opportunity in addressing the energy sustainability issue as an informatics problem where the goal is to deliver energy usage feedback to the users as a means of enabling behavioral change towards energy sustainability. In this paper we present a method to provide appliance energy usage feedback from smart meters using energy disaggregation. We put energy disaggregation in the context of a source separation and signal reconstruction problem in which we train a fully convolutional encoder decoder network to separate appliance energy usage from aggregate whole house electricity consumption data. The results show that the proposed fully convolutional encoder decoder model can achieve competitive accuracy compared with several state-of-the-art methods.
Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results
Authors: Mohamed F. Ghalwash, Ying Li, Ping Zhang, Jianying Hu
Venue: CIKM
Year: 2017

The proliferation of Electronic Health Records (EHRs) challenges data miners to discover potential and previously unknown patterns from a large collection of medical data. One of the tasks that we address in this paper is to reveal previously unknown effects of drugs on laboratory test results. We propose a method that leverages drug information to find a meaningful list of drugs that have an effect on the laboratory result. We formulate the problem as a convex non smooth function and develop a proximal gradient method to optimize it. The model has been evaluated on two important use cases: lowering low-density lipoproteins and glycated hemoglobin test results. The experimental results provide evidence that the proposed method is more accurate than the state-of-the-art method, rediscover drugs that are known to lower the levels of laboratory test results, and most importantly, discover additional potential drugs that may also lower these levels.
Finding Periodic Discrete Events in Noisy Streams
Authors: Abhirup Ghosh, Christopher Lucas, Rik Sarkar
Venue: CIKM
Year: 2017

Periodic phenomena are ubiquitous, but detecting and predicting periodic events can be difficult in noisy environments. We describe a model of periodic events that covers both idealized and realistic scenarios characterized by multiple kinds of noise. The model incorporates false-positive events and the possibility that the underlying period and phase of the events change over time. We then describe a particle filter that can efficiently and accurately estimate the parameters of the process generating periodic events intermingled with independent noise events. The system has a small memory footprint, and, unlike alternative methods, its computational complexity is constant in the number of events that have been observed. As a result, it can be applied in low-resource settings that require real-time performance over long periods of time. In experiments on real and simulated data we find that it outperforms existing methods in accuracy and can track changes in periodicity and other characteristics in dynamic event streams.
Partitioning Orders in Online Shopping Services
Authors: Sreenivas Gollapudi, Ravi Kumar, Debmalya Panigrahi, Rina Panigrahy
Venue: CIKM
Year: 2017

The rapid growth of the Internet has led to the widespread use of newer and richer models of online shopping and delivery services. The race to efficient large scale on-demand delivery has transformed such services into complex networks of shoppers (typically working in the stores), stores, and consumers. The efficiency of processing orders in stores is critical to the profitability of the business model. Motivated by this setting, we consider the following problem: given a set of shopping orders each consisting of a few items, how to best partition the orders among a given number of shoppers working for an online shopping service? Formulating this as an optimization problem, we propose a family of simple and efficient algorithms that admit natural constraints such as number of items a shopper can process in this setting. In addition to showing provable guarantees for the algorithms, we also demonstrate their efficiency in practice on real-world data, outperforming strong baselines.
Minimizing Tension in Teams
Authors: Behzad Golshan, Evimaria Terzi
Venue: CIKM
Year: 2017

In large organizations (e.g., companies, universities, etc.) individual experts with different work habits are asked to work together in order to complete projects or tasks. Oftentimes, the differences in the inherent work habits of these experts causes tension among them, which can prove detrimental for the organization's performance and functioning. The question we consider in this paper is the following: "can this tension be reduced by providing incentives to individuals to change their work habits?" We formalize this question in the definition of the k- AlterHabit problem. To the best of our knowledge we are the first to define this problem and analyze its properties. Although we show that k- AlterHabit is NP-hard, we devise polynomial-time algorithms for solving it in practice. Our algorithms are based on interesting connections that we draw between our problem and other combinatorial problems. Our experimental results demonstrate both the efficiency and the efficacy of our algorithmic techniques on a collection of real data.
Latency Reduction via Decision Tree Based Query Construction
Authors: Aman Grover, Dhruv Arya, Ganesh Venkataraman
Venue: CIKM
Year: 2017

LinkedIn as a professional network serves the career needs of 450 Million plus members. The task of job recommendation system is to nd the suitable job among a corpus of several million jobs and serve this in real time under tight latency constraints. Job search involves nding suitable job listings given a user, query and context. Typical scoring function for both search and recommendations involves evaluating a function that matches various elds in the job description with various elds in the member pro le. This in turn translates to evaluating a function with several thousands of features to get the right ranking. In recommendations, evaluating all the jobs in the corpus for all members is not possible given the latency constraints. On the other hand, reducing the candidate set could potentially involve loss of relevant jobs. We present a way to model the underlying complex ranking function via decision trees. The branches within the decision trees are query clauses and hence the decision trees can be mapped on to real time queries. We developed an o ine framework which evaluates the quality of the decision tree with respect to latency and recall. We tested the approach on job search and recommendations on LinkedIn and A/B tests show signi cant improvements in member engagement and latency. Our techniques helped reduce job search latency by over 67% and our recommendations latency by over 55%. Our techniques show 3.5% improvement in applications from job recommendations primarily due to reduced timeouts from upstream services. As of writing the approach powers all of job search and recommendations on LinkedIn.
An Euclidean Distance based on the Weighted Self-information Related Data Transformation for Nominal Data Clustering
Authors: Lei Gu, Liying Zhang, Yang Zhao
Venue: CIKM
Year: 2017

Numerical data clustering is a tractable task since well-defined numerical measures like traditional Euclidean distance can be directly used for it, but nominal data clustering is a very difficult problem because there exists no natural relative ordering between nominal attribute values. This paper mainly aims to make the Euclidean distance measure appropriate to nominal data clustering, and the core idea is to transform each nominal attribute value into numerical. This transformation method consists of three steps. In the first step, the weighted self-information, which can quantify the amount of information in attribute values, is calculated for each value in each nominal attribute. In the second step, we find k nearest neighbors for each object because k nearest neighbors of one object have close similarities with it. In the last step, the weighted self-information of each attribute value in each nominal object is modified according to the object's k nearest neighbors. To evaluate the effectiveness of our proposed method, experiments are done on 10 data sets. Experimental results demonstrate that our method not only enables the Euclidean distance to be used for nominal data clustering, but also can acquire the better clustering performance than several existing state-of-the-art approaches.
Interest Diffusion in Heterogeneous Information Network for Personalized Item Ranking
Authors: Mukul Gupta, Pradeep Kumar, Rajhans Mishra
Venue: CIKM
Year: 2017

Personalized item ranking for recommending top-N items of interest to a user is an interesting and challenging problem in e-commerce. Researchers and practitioner are continuously trying to devise new methodologies to improve the accuracy of recommendations. Recommendation problem becomes more challenging for sparse binary implicit feedback, due to the absence of explicit signals of interest and sparseness of data. In this paper, we deal with the problem of the sparseness of data and accuracy of recommendations. To address the issue, we propose an interest diffusion methodology in heterogeneous information network for items to be recommended using the meta-information related to items. In this heterogeneous information network, graph regularized interest diffusion is performed to generate personalized recommendations of top-N items. For interest diffusion, personalized weight learning is performed for different meta-information object types in the network. The experimental evaluation and comparison of the proposed methodology with the state-of-the-art techniques using the real-world datasets show the effectiveness of the proposed approach
Optimizing Email Volume For Sitewide Engagement
Authors: Rupesh Gupta, Guanfeng Liang, Rómer Rosales
Venue: CIKM
Year: 2017

In this paper we focus on the problem of optimizing email volume for maximizing sitewide engagement of an online social networking service. Email volume optimization approaches published in the past have proposed optimization of email volume for maximization of engagement metrics which are impacted exclusively by email; for example, the number of sessions that begin with clicks on links within emails. The impact of email on such downstream engagement metrics can be estimated easily because of the ease of attribution of such an engagement event to an email. However, this framework is limited in its view of the ecosystem of the networking service which comprises of several tools and utilities that contribute towards delivering value to members; with email being just one such utility. Thus, in this paper we depart from previous approaches by exploring and optimizing the contribution of email to this ecosystem. In particular, we present and contrast the differential impact of email on sitewide engagement metrics for various types of users. We propose a new email volume optimization approach which maximizes sitewide engagement metrics, such as the total number of active users. This is in sharp contrast to the previous approaches whose objective has been maximization of downstream engagement metrics. We present details of our prediction function for predicting the impact of emails on a user's activeness on the mobile or web application. We describe how certain approximations to this prediction function can be made for solving the volume optimization problem, and present results from online A/B tests.
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
Authors: Viet Ha-Thuc, Yan Yan, Xianren Wu, Vijay Dialani, Abhishek Gupta, Shakti Sinha
Venue: CIKM
Year: 2017

One key challenge in talent search is to translate complex criteria of a hiring position into a search query, while it is relatively easy for a searcher to list examples of suitable candidates for a given position. To improve search e ciency, we propose the next generation of talent search at LinkedIn, also referred to as Search By Ideal Candidates. In this system, a searcher provides one or several ideal candidates as the input to hire for a given position. The system then generates a query based on the ideal candidates and uses it to retrieve and rank results. Shifting from the traditional Query-By-Keyword to this new Query-By-Example system poses a number of challenges: How to generate a query that best describes the candidates? When moving to a completely di erent paradigm, how does one leverage previous product logs to learn ranking models and/or evaluate the new system with no existing usage logs? Finally, given the di erent nature between the two search paradigms, the ranking features typically used for Query-By-Keyword systems might not be optimal for Query- By-Example. This paper describes our approach to solving these challenges. We present experimental results con rming the e ectiveness of the proposed solution, particularly on query building and search ranking tasks. As of writing this paper, the new system has been available to all LinkedIn members.
Source Retrieval for Web-Scale Text Reuse Detection
Authors: Matthias Hagen, Martin Potthast, Payam Adineh, Ehsan Fatehifar, Benno Stein
Venue: CIKM
Year: 2017

The first step of text reuse detection addresses the source retrieval problem: given a suspicious document, a set of candidate sources from which text might have been reused have to be retrieved by querying a search engine. Afterwards, in a second step, the retrieved candidates run through a text alignment with the suspicious document in order to identify reused passages. Obviously, any true source of text reuse that is not retrieved during the source retrieval step reduces the overall recall of a reuse detector. Hence, source retrieval is a recall-oriented task, a fact ignored even by experts: Only 3 of 20 teams participating in a respective task at PAN 2012-2016 managed to find more than half of the sources, the best one achieving a recall of only~0.59. We propose a new approach that reaches a recall of~0.89---a performance gain of~51%.
Health Forum Thread Recommendation Using an Interest Aware Topic Model
Authors: Kishaloy Halder, Min-Yen Kan, Kazunari Sugiyama
Venue: CIKM
Year: 2017

We introduce a general, interest-aware topic model (IATM), in which known higher-level interests on topics expressed by each user can be modeled. We then specialize the IATM for use in consumer health forum thread recommendation by equating each user's self-reported medical conditions as interests and topics as symptoms of treatments for recommendation. The IATM additionally models the implicit interests embodied by users' textual descriptions in their profiles. To further enhance the personalized nature of the recommendations, we introduce jointly normalized collaborative topic regression (JNCTR) which captures how users interact with the various symptoms belonging to the same clinical condition.
Budgeted Task Scheduling for Crowdsourced Knowledge Acquisition
Authors: Tao Han, Hailong Sun, Yangqiu Song, Zizhe Wang, Xudong Liu
Venue: CIKM
Year: 2017

Knowledge acquisition (e.g. through labeling) is one of the most successful applications in crowdsourcing. In practice, collecting as specific as possible knowledge via crowdsourcing is very useful since specific knowledge can be generalized easily if we have a knowledge base, but it is difficult to infer specific knowledge from general knowledge. Meanwhile, tasks for acquiring more specific knowledge can be more difficult for workers, thus need more answers to infer high-quality results. Given a limited budget, assigning workers to difficult tasks will be more effective for the goal of specific knowledge acquisition. However, existing crowdsourcing task scheduling cannot incorporate the specificity of workers' answers. In this paper, we present a new framework for task scheduling with the limited budget, targeting an effective solution to more specific knowledge acquisition. We propose novel criteria for evaluating the quality of specificity-dependent answers and result inference algorithms to aggregate more specific answers with budget constraints. We have implemented our framework with real crowdsourcing data and platform, and have achieved significant performance improvement compared with existing approaches.
Keyword Search on RDF Graphs - A Query Graph Assembly Approach
Authors: Shuo Han, Lei Zou, Jeffrey Xu Yu, Dongyan Zhao
Venue: CIKM
Year: 2017

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently. Based on the input keywords, we first obtain the elementary query graph building blocks, such as entity/class vertices and predicate edges. Then, we formally define the query graph assembly (QGA) problem. Unfortunately, we prove theoretically that QGA is a NP-complete problem. In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm. The algorithm's time complexity is O(k2l ... l3l), where l is the number of the keywords and k is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword. Although QGA is intractable, both l and k are small in practice. Furthermore, the algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs. Experiments on DBpedia and Freebase confirm the superiority of our system on both effectiveness and efficiency.
Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care
Authors: Casper Hansen, Christian Hansen, Stephen Alstrup, Christina Lioma
Venue: CIKM
Year: 2017

We present an ensemble learning method that predicts large increases in the hours of home care received by citizens. The method is supervised, and uses different ensembles of either linear (logistic regression) or non-linear (random forests) classifiers. Experiments with data available from 2013 to 2017 for every citizen in Copenhagen receiving home care (27,775 citizens) show that prediction can achieve state of the art performance as reported in similar health related domains (AUC=0.715). We further find that competitive results can be obtained by using limited information for training, which is very useful when full records are not accessible or available. Smart city analytics does not necessarily require full city records. To our knowledge this preliminary study is the first to predict large increases in home care for smart city analytics.