OpenRaiser/Intern-Atlas huggingface dataset:
Efficient and Effective Query Expansion for Web Search
Authors: Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Roberto Trani, Rossano Venturini
Venue: CIKM
Year: 2018

Query Expansion (QE) techniques expand the user queries with additional terms, e.g., synonyms and acronyms, to enhance the system recall. State-of-the-art solutions employ machine learning methods to select the most suitable terms. However, most of them neglect the cost of processing the expanded queries, thus selecting effective, yet very expensive, terms. The goal of this paper is to enable QE in scenarios with tight time constraints proposing a QE framework based on structured queries and efficiency-aware term selection strategies. In particular, the proposed expansion selection strategies aim at capturing the efficiency and the effectiveness of the expansion candidates, as well as the dependencies among them. We evaluate our proposals by conducting an extensive experimental assessment on real-world search engine data and public TREC data. Results confirm that our approach leads to a remarkable efficiency improvement w.r.t. the state-of-the-art: a reduction of the retrieval time up to 30 times, with only a small loss of effectiveness.
Homepage Augmentation by Predicting Links in Heterogenous Networks
Authors: Jianming Lv, Jiajie Zhong, Weihang Chen, Qinzhe Xiao, Zhenguo Yang, Qing Li
Venue: CIKM
Year: 2018

Scholars' homepages are important places to show personal research interest and academic achievement through the Web. However, according to our observation, only a small portion of scholars update their publications and related events on their homepages in time. In this paper, we propose a homepage augmentation technique, which automatically shows the newest academic events related to a scholar on his/her homepage. Specifically, we model the relations between homepages and the events collected from the Web as a complex heterogenous network, and propose an Embedding-based Heterogenous random Walk algorithm, namely EHWalk, to predict the links between homepages and events. Compared with existing embedding-based link prediction algorithms, EHWalk supports more efficient modeling of complex heterogenous relations in a dynamically changing network, which helps link the massive new updated events to homepages precisely and efficiently. Comprehensive experiments on a real-world dataset are conducted and the results show that our algorithm can achieve both good effectiveness and efficiency for real-world deployment.
DiVE: Diversifying View Recommendation for Visual Data Exploration
Authors: Rischan Mafrur, Mohamed A. Sharaf, Hina A. Khan
Venue: CIKM
Year: 2018

To support effective data exploration, there has been a growing interest in developing solutions that can automatically recommend data visualizations that reveal interesting and useful data-driven insights. In such solutions, a large number of possible data visualization views are generated and ranked according to some metric of importance (e.g., a deviation-based metric), then the top-k most important views are recommended. However, one drawback of that approach is that it often recommends similar views, leaving the data analyst with a limited amount of gained insights. To address that limitation, in this work we posit that employing diversification techniques in the process of view recommendation allows eliminating that redundancy and provides a good and concise coverage of the possible insights to be discovered. To that end, we propose a hybrid objective utility function, which captures both the importance, as well as the diversity of the insights revealed by the recommended views. While in principle, traditional diversification methods (e.g., Greedy Construction) provide plausible solutions under our proposed utility function, they suffer from a significantly high query processing cost. In particular, directly applying such methods leads to a "process-first-diversify-next" approach, in which all possible data visualization are generated first via executing a large number of aggregate queries. To address that challenge, we propose an integrated scheme called DiVE, which efficiently selects the top-k recommended view based on our hybrid utility function. DiVE leverages the properties of both the importance and diversity metrics to prune a large number of query executions without compromising the quality of recommendations. Our experimental evaluation on real datasets shows the performance gains provided by DiVE.
Online Learning for Non-Stationary A/B Tests
Authors: Andrés Muñoz Medina, Sergei Vassilvitskii, Dong Yin
Venue: CIKM
Year: 2018

The rollout of new versions of a feature in modern applications is a manual multi-stage process, as the feature is released to ever larger groups of users, while its performance is carefully monitored. This kind of A/B testing is ubiquitous, but suboptimal, as the monitoring requires heavy human intervention, is not guaranteed to capture consistent, but short-term fluctuations in performance, and is inefficient, as better versions take a long time to reach the full population. In this work we formulate this question as that of expert learning, and give a new algorithm Follow-The-Best-Interval, FTBI, that works in dynamic, non-stationary environments. Our approach is practical, simple, and efficient, and has rigorous guarantees on its performance. Finally, we perform a thorough evaluation on synthetic and real world datasets and show that our approach outperforms current state-of-the-art methods.
How Consistent is Relevance Feedback in Exploratory Search?
Authors: Alan Medlar, Dorota Glowacka
Venue: CIKM
Year: 2018

Search activities involving knowledge acquisition, investigation and synthesis are collectively known as exploratory search. Exploratory search is challenging for users, who may be unable to formulate search queries, have ill-defined search goals or may even struggle to understand search results. To ameliorate these difficulties, reinforcement learning-based information retrieval systems were developed to provide adaptive support to users. Reinforcement learning is used to build a model of user intent based on relevance feedback provided by the user. But how reliable is relevance feedback in this context? To answer this question, we developed a novel permutation-based metric for scoring the consistency of relevance feedback. We used this metric to perform a retrospective analysis of interaction data from lookup and exploratory search experiments. Our analysis shows that for lookup search relevance judgments are highly consistent, supporting previous findings that relevance feedback improves retrieval performance. For exploratory search, however, the distribution of consistency scores shows considerable inconsistency.
Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems
Authors: Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, Fernando Diaz
Venue: CIKM
Year: 2018

Two-sided marketplaces are platforms that have customers not only on the demand side (e.g. users), but also on the supply side (e.g. retailer, artists). While traditional recommender systems focused specifically towards increasing consumer satisfaction by providing relevant content to consumers, two-sided marketplaces face the problem of additionally optimizing for supplier preferences, and visibility. Indeed, the suppliers would want afair opportunity to be presented to users. Blindly optimizing for consumer relevance may have a detrimental impact on supplier fairness. Motivated by this problem, we focus on the trade-off between objectives of consumers and suppliers in the case of music streaming services, and consider the trade-off betweenrelevance of recommendations to the consumer (i.e. user) andfairness of representation of suppliers (i.e. artists) and measure their impact on consumersatisfaction. We propose a conceptual and computational framework using counterfactual estimation techniques to understand, and evaluate different recommendation policies, specifically around the trade-off between relevance and fairness, without the need for running many costly A/B tests. We propose a number of recommendation policies which jointly optimize relevance and fairness, thereby achieving substantial improvement in supplier fairness without noticeable decline in user satisfaction. Additionally, we consider user disposition towards fair content, and propose a personalized recommendation policy which takes into account consumer's tolerance towards fair content. Our findings could guide the design of algorithms powering two-sided marketplaces, as well as guide future research on sophisticated algorithms for joint optimization of user relevance, satisfaction and fairness.
An Attentive Interaction Network for Context-aware Recommendations
Authors: Lei Mei, Pengjie Ren, Zhumin Chen, Liqiang Nie, Jun Ma, Jian-Yun Nie
Venue: CIKM
Year: 2018

Context-aware Recommendations (CARS) have attracted a lot of attention recently because of the impact of contextual information on user behaviors. Recent state-of-the-art methods represent the relations between users/items and contexts as a tensor, with which it is difficult to distinguish the impacts of different contextual factors and to model complex, non-linear interactions between contexts and users/items. In this paper, we propose a novel neural model, named Attentive Interaction Network (AIN), to enhance CARS through adaptively capturing the interactions between contexts and users/items. Specifically, AIN contains an Interaction-Centric Module to capture the interaction effects of contexts on users/items; a User-Centric Module and an Item-Centric Module to model respectively how the interaction effects influence the user and item representations. The user and item representations under interaction effects are combined to predict the recommendation scores. We further employ effect-level attention mechanism to aggregate multiple interaction effects. Extensive experiments on two rating datasets and one ranking dataset show that the proposed AIN outperforms state-of-the-art CARS methods. In addition, we also find that AIN provides recommendations with better explanation ability with respect to contexts than the existing approaches.
Weakly-Supervised Neural Text Classification
Authors: Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han
Venue: CIKM
Year: 2018

Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.
Compiling Questions into Balanced Quizzes about Documents
Authors: Cristina Menghini, Jessica Dehler-Zufferey, Robert West
Venue: CIKM
Year: 2018

In the educational framework, knowledge assessment is a critical component, and quizzes (sets of questions with concise answers) are a popular tool for this purpose. This paper focuses on the generation of balanced quizzes, i.e., quizzes that relate to a given set of documents, and to the central concepts described by the documents, in an evenly distributed manner. Our approach leverages a graph representing the relationships between questions, documents, and concepts, and phrases quiz construction as a node selection problem in this graph. We provide algorithms for constructing the graph and for selecting a good set of quiz questions. In our concrete implementation, we build quizzes for a collection of Wikipedia articles and evaluate them both with simulated students and with real human quiz takers, finding that our balanced quizzes are better suited at determining which articles the user has not read (corresponding to their knowledge gaps) than reasonable baselines.
Efficient Pipeline Processing of Crowdsourcing Workflows
Authors: Ken Mizusawa, Keishi Tajima, Masaki Matsubara, Toshiyuki Amagasa, Atsuyuki Morishima
Venue: CIKM
Year: 2018

This paper addresses the pipeline processing of sequential workflows in crowdsourcing. Sequential workflows consisting of several subtasks are ubiquitous in crowdsourcing. Our approach is to control the budget distribution to subtasks in order to balance the execution speed of the subtasks and to improve throughput of overall sequential workflows. As we cannot control the price for earlier steps retrospectively in the stepwise batch execution, we explore pipeline processing schemes. Our experimental results show that our pipeline processing scheme with price control achieves significantly higher throughput of sequential workflows.
Contrasting Search as a Learning Activity with Instructor-designed Learning
Authors: Felipe Moraes, Sindunuraga Rikarno Putra, Claudia Hauff
Venue: CIKM
Year: 2018

The field of Search as Learning addresses questions surrounding human learning during the search process. Existing research has largely focused on observing how users with learning-oriented information needs behave and interact with search engines. What is not yet quantified is the extent to which search is a viable learning activity compared to instructor-designed learning. Can a search session be as effective as a lecture video - our instructor-designed learning artefact - or learning? To answer this question, we designed a user study that pits instructor-designed learning (a short high-quality video lecture as commonly found in online learning platforms) against three instances of search, specifically (i) single-user search, (ii) search as a support tool for instructor-designed learning, and, (iii) collaborative search. We measured the learning gains of 151 study participants in a vocabulary learning task and report three main results: (i) lecture video watching yields up to 24% higher learning gains than single-user search, (ii) collaborative search for learning does not lead to increased learning, and (iii) lecture video watching supported by search leads up to a 41% improvement in learning gains over instructor-designed learning without a subsequent search phase.
Point Symmetry-based Deep Clustering
Authors: José G. Moreno
Venue: CIKM
Year: 2018

Clustering is a central task in unsupervised learning. Recent advances that perform clustering into learned deep features (such as DEC[14], IDEC [6] or VaDe [10]) have shown improvements over classical algorithms, but most of them are based on the Euclidean distance. Moreover, symmetry-based distances have shown to be a powerful tool to distinguish symmetric shapes -- such as circles, ellipses, squares, etc. This paper presents an adaptation of symmetry-based distances into deep clustering algorithms, named SymDEC. Our results show that the proposed strategy outperforms significantly the existing Euclidean-based deep clustering as well as recent symmetry-based algorithms in several of the synthetic symmetric and UCI studied datasets.
Low-Complexity Supervised Rank Fusion Models
Authors: André Mourão, João Magalhães
Venue: CIKM
Year: 2018

Combining multiple retrieval functions can lead to notable gains in retrieval performance. Learning to Rank (LETOR) techniques achieve outstanding retrieval results, by learning models with no bounds on model complexity. Often, minor retrieval gains are attained at a significant cost in model complexity. This paper focuses on the research question:can less complex models achieve results comparable to LETOR models? In this paper, we investigate an approach for the selection and fusion of rank lists with low-complexity models. The described Learning to Fuse (L2F) algorithm, is a supervised rank fusion procedure that controls the model complexity by discarding rank lists that bring minor improvements to final rank. Evaluation results, on two different datasets, show that it is indeed possible to achieve a retrieval performance comparable to LETOR methods, using only 3-5% of the rank lists of the number of rank lists used by LETOR methods.
Mining & Summarizing E-petitions for Enhanced Understanding of Public Opinion
Authors: Shreshtha Mundra, Sachin Kumar, Manjira Sinha, Sandya Mannarswamy
Venue: CIKM
Year: 2018

Today electronic communications have become the prime medium for people to express their opinions and influence the policy preferences. One such popular channel reflecting the voice of the masses is electronic petitions. To understand people's perspective on various issues it is important to know what petitions say. However, due to the sheer volume of the petitions it is difficult to process each petition manually. As each petition talks about a different issue, prioritizing on over other is difficult. To alleviate these challenges, we present an end to end system for generating comprehensive and concise summaries from e-petitions. A petition contains multiple aspects, the core problem, evidence(s) in support of the problem and potential solutions. Therefore, it is imperative that an useful summary should contain information about all these three aspects explicitly. To achieve this, our system generates three aspect based summaries for each petition for better understanding. We also introduce a new annotated petition dataset, developed through crowd-sourcing, that served as gold standard. Our model is tested through quantitative and qualitative evaluations.
PSLSH: An Index Structure for Efficient Execution of Set Queries in High-Dimensional Spaces
Authors: Parth Nagarkar, K. Selçuk Candan
Venue: CIKM
Year: 2018

Efficient implementations of range and nearest neighbor queries are critical in many large multimedia applications. Locality Sensitive Hashing (LSH) is a popular technique for performing approximate searches in high-dimensional multimedia, such as image or sensory data. Often times, these multimedia data are represented as a collection of important spatio-temporal features which are extracted by using localized feature extraction algorithms. When a user wants to search for a given entity (object, event, or observation), individual similarity search queries, which collectively form a set query, need to be performed on the features that represent the particular search entity. Existing LSH techniques require that users provide an accuracy guarantee for each query in the set query, instead of an overall guarantee for the entire set query, which can lead to misses or wasteful work. We propose a novel index structure, Point Set LSH (PSLSH), which is able to execute a similarity search for a given set of search points in the high-dimensional space with a user-provided guarantee for the entire set query. Experimental evaluation shows significant gains in efficiency and accuracy trade-offs for executing set queries in high-dimensional spaces.
Type Prediction Combining Linked Open Data and Social Media
Authors: Yaroslav Nechaev, Francesco Corcoglioniti, Claudio Giuliano
Venue: CIKM
Year: 2018

Linked Open Data (LOD) and social media often contain the representations of the same real-world entities, such as persons and organizations. These representations are increasingly interlinked, making it possible to combine and leverage both LOD and social media data in prediction problems, complementing their relative strengths: while LOD knowledge is highly structured but also scarce and obsolete for some entities, social media data provide real-time updates and increased coverage, albeit being mostly unstructured. In this paper, we investigate the feasibility of using social media data to perform type prediction for entities in a LOD knowledge graph. We discuss how to gather training data for such a task, and how to build an efficient domain-independent vector representation of entities based on social media data. Our experiments on several type prediction tasks using DBpedia and Twitter data show the effectiveness of this representation, both alone and combined with knowledge graph-based features, suggesting its potential for ontology population.
Privacy Protection for Flexible Parametric Survival Models
Authors: Thông T. Nguyên, Siu Cheung Hui
Venue: CIKM
Year: 2018

Data privacy is a major concern in modern society. In this work, we propose two solutions to the privacy-preserving problem of regression models on medical data. We focus on flexible parametric models which are powerful alternatives to the well-known Cox regression model. For the first approach, we propose a sampling mechanism which guarantees differential privacy for flexible parametric survival models. We first transform the likelihood function of the models to guarantee that likelihood values are bounded. We then use a Hamiltonian Monte-Carlo sampler to sample a random parameter vector from the posterior distribution. As a result, this random vector satisfies the requirement for differential privacy. For the second approach, as predictions with high accuracy and high confidence are very important for medical applications, we propose a mechanism which protects privacy by randomly perturbing the posterior distribution. We can then use the sampler to draw multiple random samples of the perturbed posterior to estimate the credible intervals of the parameters. The proposed mechanism does not guarantee differential privacy for the perturbed posterior. However, it allows controlling the contribution of each individual data record to the posterior. In the worst case scenario, when all data records are revealed except the target data record, the random noise added to the posterior would make it extremely difficult to obtain the target data record. The experiments conducted on two real datasets show that our proposed approaches outperform state-of-the-art methods in predicting the survival rate of individuals.
BBoxDB - A Scalable Data Store for Multi-Dimensional Big Data
Authors: Jan Kristof Nidzwetzki, Ralf Hartmut Güting
Venue: CIKM
Year: 2018

BBoxDB is a distributed and highly available key-bounding-box-value store which enhances the classical key-value data model with an axis-parallel bounding box. The bounding box describes the location of the values in an n-dimensional space, and enables BBoxDB to efficiently distribute multi-dimensional data across a cluster of nodes. Well-known geometric algorithms (such as the K-D Tree) are used to create distribution regions (multi-dimensional shards). Distribution regions are created dynamically, based on the stored data. BBoxDB stores data of multiple tables co-partitioned, which enables efficient distributed spatial joins. Spatial joins on co-partitioned tables can be executed without data shuffling between nodes. A two-level index structure is employed to retrieve stored data quickly. We demonstrate the interaction with the system, the dynamic creation of distribution regions and the data redistribution feature of BBoxDB.