OpenRaiser/Intern-Atlas huggingface dataset:
Answering Twitter Questions: a Model for Recommending Answerers through Social Collaboration
Authors: Laure Soulier, Lynda Tamine, Gia-Hung Nguyen
Venue: CIKM
Year: 2016

In this paper, we specifically consider the challenging task of solving a question posted on Twitter. The latter generally remains unanswered and most of the replies, if any, are only from members of the questioner's neighborhood. As outlined in previous work related to community Q&A, we believe that question-answering is a collaborative process and that the relevant answer to a question post is an aggregation of answer nuggets posted by a group of relevant users. Thus, the problem of identifying the relevant answer turns into the problem of identifying the right group of users who would provide useful answers and would possibly be willing to collaborate together in the long-term. Accordingly, we present a novel method, called CRAQ, that is built on the collaboration paradigm and formulated as a group entropy optimization problem. To optimize the quality of the group, an information gain measure is used to select the most likely "informative" users according to topical and collaboration likelihood predictive features. Crowd-based experiments performed on two crisis-related Twitter datasets demonstrate the effectiveness of our collaborative-based answering approach.
Incorporating Risk-Sensitiveness into Feature Selection for Learning to Rank
Authors: Daniel Xavier de Sousa, Sérgio Daniel Canuto, Thierson Couto Rosa, Wellington Santos Martins, Marcos André Gonçalves
Venue: CIKM
Year: 2016

Learning to Rank (L2R) is currently an essential task in basically all types of information systems given the huge and ever increasing amount of data made available. While many solutions have been proposed to improve L2R functions, relatively little attention has been paid to the task of improving the quality of the feature space. L2R strategies usually rely on dense feature representations, which contain noisy or redundant features, increasing the cost of the learning process, without any benefits. Although feature selection (FS) strategies can be applied to reduce dimensionality and noise, side effects of such procedures have been neglected, such as the risk of getting very poor predictions in a few (but important) queries. In this paper we propose multi-objective FS strategies that optimize both aspects at the same time: ranking performance and risk-sensitive evaluation. For this, we approximate the Pareto-optimal set for multi-objective optimization in a new and original application to L2R. Our contributions include novel FS methods for L2R which optimize multiple, potentially conflicting, criteria. In particular, one of the objectives (risk-sensitive evaluation) has never been optimized in the context of FS for L2R before. Our experimental evaluation shows that our proposed methods select features that are more effective (ranking performance) and low-risk than those selected by other state-of-the-art FS methods.
A Comparative Study of Query-biased and Non-redundant Snippets for Structured Search on Mobile Devices
Authors: Nikita V. Spirin, Alexander S. Kotov, Karrie G. Karahalios, Vassil Mladenov, Pavel A. Izhutov
Venue: CIKM
Year: 2016

To investigate what kind of snippets are better suited for structured search on mobile devices, we built an experimental mobile search application and conducted a task-oriented interactive user study with 36 participants. Four different versions of a search engine result page (SERP) were compared by varying the snippet type (query-biased vs. non-redundant) and the snippet length (two vs. four lines per result). We adopted a within-subjects experiment design and made each participant do four realistic search tasks using different versions of the application. During the study sessions, we collected search logs, "think-aloud" comments, and post-task surveys. Each session was finalized with an interview. We found that with non-redundant snippets the participants were able to complete the tasks faster and find more relevant results. Most participants preferred non-redundant snippets and wanted to see more information about each result on the SERP for any snippet type. Yet, the participants felt that the version with query-biased snippets was easier to use. We conclude with a set of practical design recommendations.
Modeling Customer Engagement from Partial Observations
Authors: Jelena Stojanovic, Djordje Gligorijevic, Zoran Obradovic
Venue: CIKM
Year: 2016

It is of high interest for a company to identify customers expected to bring the largest profit in the upcoming period. Knowing as much as possible about each customer is crucial for such predictions. However, their demographic data, preferences, and other information that might be useful for building loyalty programs is often missing. Additionally, modeling relations among different customers as a network can be beneficial for predictions at an individual level, as similar customers tend to have similar purchasing patterns. We address this problem by proposing a robust framework for structured regression on deficient data in evolving networks with a supervised representation learning based on neural features embedding. The new method is compared to several unstructured and structured alternatives for predicting customer behavior (e.g. purchasing frequency and customer ticket) on user networks generated from customer databases of two companies from different industries. The obtained results show 4% to 130% improvement in accuracy over alternatives when all customer information is known. Additionally, the robustness of our method is demonstrated when up to 80% of demographic information was missing where it was up to several folds more accurate as compared to alternatives that are either ignoring cases with missing values or learn their feature representation in an unsupervised manner.
Approximate Aggregates in Oracle 12C
Authors: Hong Su, Mohamed Zaït, Vladimir Barrière, Joseph Torres, Andre Cavalheiro Menck
Venue: CIKM
Year: 2016

New generation of analytic applications emerged to process data generated from non conventional sources. The challenge for the traditional database systems is that the data sets are very large and keep increasing at a very high rate while the application users have higher performance expectations. The most straightforward response to this challenge is to deploy larger hardware configurations making the solution very expensive and not acceptable for most cases. Alternative solutions fall into two categories: reduce the data set using sampling techniques or reduce the computational complexity of expensive database operations by using alternative algorithms. Alternative algorithms considered in this paper are approximate aggregates that perform a lot better at the cost of reduced and tolerable accuracy. In Oracle 12C we introduced approximate aggregates of expensive aggregate functions that are very common in analytic applications, that is, approximate count distinct and approximate percentile. The performance is improved in two ways. First, the approximate aggregates use bounded memory, often eliminating the need to use temporary storage which results in significant performance improvement over the exact aggregates. Second, we provide materialized view support that allows users to store pre-computed results of approximate aggregates. These results can be rolled up to answer queries on different dimensions (such rollup is not possible for exact aggregates).
DePP: A System for Detecting Pages to Protect in Wikipedia
Authors: Kelsey Suyehira, Francesca Spezzano
Venue: CIKM
Year: 2016

Wikipedia is based on the idea that anyone can make edits to the website in order to create reliable and crowd-sourced content. Yet with the cover of internet anonymity, some users make changes to the website that do not align with Wikipedia's intended uses. For this reason, Wikipedia allows for some pages of the website to become protected, where only certain users can make revisions to the page. This allows administrators to protect pages from vandalism, libel, and edit wars. However, with over five million pages on Wikipedia, it is impossible for administrators to monitor all pages and manually enforce page protection. In this paper we consider for the first time the problem of deciding whether a page should be protected or not in a collaborative environment such as Wikipedia. We formulate the problem as a binary classification task and propose a novel set of features to decide which pages to protect based on (i) users page revision behavior and (ii) page categories. We tested our system, called DePP, on a new dataset we built consisting of 13.6K pages (half protected and half unprotected) and 1.9M edits. Experimental results show that DePP reaches 93.24% classification accuracy and significantly improves over baselines.
A Neural Network Approach to Quote Recommendation in Writings
Authors: Jiwei Tan, Xiaojun Wan, Jianguo Xiao
Venue: CIKM
Year: 2016

Quote is a language phenomenon of transcribing the saying of someone else. Proper usage of quote can usually make the statement more elegant and convincing. However, the ability of quote usage is usually limited by the amount of quotes one remembers or knows. Quote recommendation is a task of exploiting abundant quote repositories to help people make better use of quotes while writing. The task is different from conventional recommendation tasks due to the characteristic of quote. A pilot study has explored this task by using a learning to rank framework and manually designed features. However, it is still hard to model the meaning of a quote, which is an interesting and challenging problem. In this paper, we propose a neural network approach based on LSTMs to the quote recommendation task. We directly learn the distributed meaning representations for the contexts and the quotes, and then measure the relevance based on the meaning representations. In particular, we try to represent the words in quotes with specific embeddings, according to the contexts, topics and even author preferences of the quotes. Experimental results on a large dataset show that our proposed approach achieves the state-of-the-art performance and it outperforms several strong baselines.
BigNet 2016: First Workshop on Big Network Analytics
Authors: Jie Tang, Keke Cai, Zhong Su, Hanghang Tong, Michalis Vazirgiannis, Yang Yang
Venue: CIKM
Year: 2016

The first ACM international workshop on big network analytics is held in Indianapolis, Indiana, USA on October 24, 2016 and co-located with the ACM 25th Conference on Information and Knowledge Management (CIKM). The main objective of the workshop is to provide a forum for presenting the most recent advances in mining big networks to unearth rich knowledge. It is related to information retrieval, Web mining, social network analysis, and computational advertising. The anticipated outcome includes a fruitful discussion about the emerging challenges in this field, the development of novel theories for mining big networks, and motivating the interesting applications. The broader anticipated outcome includes: fostering future research directions, publishing high quality papers, attracting new researchers to this field, and concrete solutions to the existing problems.
ConHub: A Metadata Management System for Docker Containers
Authors: Chris Xing Tian, Aditya Pan, Yong Chiang Tay
Venue: CIKM
Year: 2016

For many years now, enterprises and cloud providers have been using virtualization to run their workloads. Until recently, this means running an application in a virtual machine (hardware virtualization). However, virtual machines are increasingly replaced by containers (operating system virtualization), as evidenced by the rapid rise of Docker. A containerized software environment can generate a large amount of metadata. If properly managed, these metadata can greatly facilitate the management of containers themselves. This demonstration introduces ConHub, a PostgreSQL-based container metadata management system. Visitors will see that (1) ConHub has a language CQL that supports Docker commands; (2) it has a user-friendly interface for querying and visualizing container relationships; and (3) they can use CQL to formulate sophisticated queries to facilitate container management.
Deep Match between Geology Reports and Well Logs Using Spatial Information
Authors: Bin Tong, Martin Klinkigt, Makoto Iwayama, Yoshiyuki Kobayashi, Anshuman Sahu, Ravigopal Vennelakanti
Venue: CIKM
Year: 2016

In the shale oil & gas industry, operators are looking toward big data and new analytics tools and techniques to optimize operations and reduce cost. Formation evaluation is one of the most crucial steps before the fracturing operation. To assist engineers in understanding the subsurface and in turn make optimal operations, we focus on learning semantic relations between geology reports and well logs, which are collected during down-hole drilling. The challenges are how to represent the features of the geology reports and the well logs collected at measured depths and how to effectively embed them into a common feature space. We propose both linear and nonlinear (artificial neural network) models to achieve such an embedding. Extensive validations are conducted on public well data of North Dakota in the United States. We empirically discover that both geology reports and well logs follow a neighborhood property measured by geological distance. We show that this spatial information is highly effective in both the linear and nonlinear models and our nonlinear model with the spatial information performs the best among the state-of-the-art methods.
Why Did You Cover That Song?: Modeling N-th Order Derivative Creation with Content Popularity
Authors: Kosetsu Tsukuda, Masahiro Hamasaki, Masataka Goto
Venue: CIKM
Year: 2016

Many amateur creators now create derivative works and put them on the web. Although there are several factors that inspire the creation of derivative works, such factors cannot usually be observed on the web. In this paper, we propose a model for inferring latent factors from sequences of derivative work posting events. We assume a sequence to be a stochastic process incorporating the following three factors: (1) the original work's attractiveness, (2) the original work's popularity, and (3) the derivative work's popularity. To characterize content popularity, we use content ranking data and incorporate rank-biased popularity based on the creators' browsing behavior. Our main contributions are three-fold: (1) to the best of our knowledge, this is the first study modeling derivative creation activity, (2) by using a real-world dataset of music-related derivative work creation to evaluate our model, we showed the effectiveness of adopting all three factors to model derivative creation activity and onsidering creators' browsing behavior, and (3) we carried out qualitative experiments and showed that our model is useful in analyzing derivative creation activity in terms of category characteristics, temporal development of factors that trigger derivative work posting events, etc.
Detecting and Ranking Conceptual Links between Texts Using a Knowledge Base
Authors: Martin Tutek, Goran Glavas, Jan Snajder, Natasa Milic-Frayling, Bojana Dalbelo Basic
Venue: CIKM
Year: 2016

Recent research has explored the use of Knowledge Bases (KBs) to represent documents as subgraphs of a KB concept graph and define metrics to characterize semantic relatedness of documents in terms of properties of the document concept graphs. However, none of the studies so far have examined to what degree such metrics capture a user-perceived relatedness of documents. Considering the users' explanations of how pairs of documents are related, the aim is to identify concepts in a KB graph that express the same notion of document relatedness. Our algorithm generates paths through the KB graph that originate from the terms in two documents. KB concepts where these paths intersect capture the semantic relatedness of the two starting terms and therefore the two documents. We consider how such intersecting concepts relate to the concepts in the users' explanations. The higher the users' concepts appear in the ranked list of intersecting concepts, the better the method in capturing the users' notion of document relatedness. Our experiments show that our approach outperforms a simpler graph method that uses properties of the concept nodes alone.
Learning to Rank Non-Factoid Answers: Comment Selection in Web Forums
Authors: Kateryna Tymoshenko, Daniele Bonadiman, Alessandro Moschitti
Venue: CIKM
Year: 2016

Recent initiatives in IR community have shown the importance of going beyond factoid Question Answering (QA) in order to design useful real-world applications. Questions asking for descriptions or explanations are much more difficult to be solved, e.g., the machine learning models cannot focus on specific answer words or their lexical type. Thus, researchers have started to explore powerful methods for feature engineering. Two of the most promising methods are convolution tree kernels (CTKs) and convolutional neural networks (CNNs) as they have been shown to obtain high performance in the task of answer sentence selection in factoid QA. In this paper, we design state-of-the-art models for non-factoid QA also carried out on noisy data. In particular, we study and compare models for comment selection in a community QA (cQA) scenario, where the majority of questions regard descriptions or explanations. To deal with such complex task, we incorporate relational information holding between questions and comments as well as domain-specific features into both convolutional models above.
Building Industry-specific Knowledge Bases
Authors: Shivakumar Vaithyanathan
Venue: CIKM
Year: 2016

Building industry-specific knowledge bases relies heavily on collecting and representing domain knowledge over time. Domain knowledge includes: (1) the logical schema, constraints and domain vocabulary of the application, (2) the models and algorithms to populate instances of that schema, and (3) the data necessary to build and maintain those models and algorithms. In IBM Watson we are using an ontology-driven approach for the creation and consumption of industry-specific knowledge bases. The creation of such knowledge bases involves well known building blocks: natural language processing, entity resolution, data transformation, etc. It is critical that the models and algorithms that implement these building blocks be transparent and optimizable for efficient execution.
Updating an Existing Social Graph Snapshot via a Limited API
Authors: Norases Vesdapunt, Hector Garcia-Molina
Venue: CIKM
Year: 2016

We study the problem of graph tracking with limited information. In this paper, we focus on updating a social graph snapshot. Say we have an existing partial snapshot, G1, of the social graph stored at some system. Over time G1 becomes out of date. We want to update G1 through a public API to the actual graph, restricted by the number of API calls allowed. Periodically recrawling every node in the snapshot is prohibitively expensive. We propose a scheme where we exploit indegrees and outdegrees to discover changes to the actual graph. When there is ambiguity, we probe the graph and verify edges. We propose a novel strategy designed for limited information that can be adapted to different levels of staleness. We evaluate our strategy against recrawling on real datasets and show that it saves an order of magnitude of API calls while introducing minimal errors.
Towards the Effective Linking of Social Media Contents to Products in E-Commerce Catalogs
Authors: Henry S. Vieira, Altigran S. da Silva, Pável Calado, Marco Cristo, Edleno Silva de Moura
Venue: CIKM
Year: 2016

Online social media has become an essential part of our life. This media is often characterized by its diverse content, which is produced by ordinary users. The potential to easily express ideas and opinions has made social media a source of valuable information on a variety of topics. In particular, information containing comments about consumer products has become prevalent. Here, we are interested in linking products mentioned in unstructured user-generated content, namely open discussion forums, to their respective entities in consumer product catalogs. Among the issues associated with this task, ambiguity is a particularly hard problem, as users typically refer to the same product using many different forms and different products may share the same form. We argue that this problem can be effectively solved using a set of evidences that can be easily extracted from social media content and product descriptions. To achieve this, we show which features should be used, how they can be extracted, and then how to combine them through machine learning techniques. Experiments in three different product categories and two different datasets demonstrate that all the sources of evidence here proposed are important, while contextual information is fundamental to achieve higher levels of precision. In fact, our method, although straightforward, was able to achieve an average improvement of 0.17 in precision and 0.13 in F1, when compared to the current state-of-the-art solution.
A Self-Learning and Online Algorithm for Time Series Anomaly Detection, with Application in CPU Manufacturing
Authors: Xing Wang, Jessica Lin, Nital Patel, Martin W. Braun
Venue: CIKM
Year: 2016

The problem of anomaly detection in time series has received a lot of attention in the past two decades. However, existing techniques cannot locate where the anomalies are within anomalous time series, or they require users to provide the length of potential anomalies. To address these limitations, we propose a self-learning online anomaly detection algorithm that automatically identifies anomalous time series, as well as the exact locations where the anomalies occur in the detected time series. We evaluate our approach on several real datasets, including two CPU manufacturing data from Intel. We demonstrate that our approach can successfully detect the correct anomalies without requiring any prior knowledge about the data.
Duer: Intelligent Personal Assistant
Authors: Haifeng Wang
Venue: CIKM
Year: 2016

Intelligent personal assistant is widely recognized as a more natural and efficient way of human-computer interaction, which has attracted extensive interests from both academia and industry. In this talk, I describe Duer, Baidu's intelligent personal assistant. In particular, I would like to focus on the following three features. Firstly, Duer comprehensively understands people's requirements via multiple channels, including not only explicit utterances, but also user models and rich contexts. Duer's user models are learnt from users' interaction history, and the rich contexts consist of temporal and geographical information, as well as the foregoing dialogues. Secondly, Duer meets diverse requirements with a range of instruments, such as chatting, information provision, reminder service, etc. These instruments are implemented based on mining the big data of web pages, applications, and user logs, which are then seamlessly integrated in the dialogue flow. Thirdly, Duer features multi-modal interaction, which allows people to interact with it by means of texts, speech, and images. We believe the above features will enable Duer to become a better and distinguished intelligent assistant for each of you.