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  • Fine-Grained Privacy Detection with Graph-Regularized Hierarchical Attentive Representation Learning
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-09-16
    Xiaolin Chen; Xuemeng Song; Ruiyang Ren; Lei Zhu; Zhiyong Cheng; Liqiang Nie

    Due to the complex and dynamic environment of social media, user generated contents (UGCs) may inadvertently leak users’ personal aspects, such as the personal attributes, relationships and even the health condition, and thus place users at high privacy risks. Limited research efforts, thus far, have been dedicated to the privacy detection from users’ unstructured data (i.e., UGCs). Moreover, existing

  • Explaining Text Matching on Neural Natural Language Inference
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-09-16
    Youngwoo Kim; Myungha Jang; James Allan

    Natural language inference (NLI) is the task of detecting the existence of entailment or contradiction in a given sentence pair. Although NLI techniques could help numerous information retrieval tasks, most solutions for NLI are neural approaches whose lack of interpretability prohibits both straightforward integration and diagnosis for further improvement. We target the task of generating token-level

  • Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-09-11
    Maristella Agosti; Stefano Marchesin; Gianmaria Silvello

    The semantic mismatch between query and document terms—i.e., the semantic gap—is a long-standing problem in Information Retrieval (IR). Two main linguistic features related to the semantic gap that can be exploited to improve retrieval are synonymy and polysemy. Recent works integrate knowledge from curated external resources into the learning process of neural language models to reduce the effect

  • MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-09-10
    Chang Li; Ilya Markov; Maarten De Rijke; Masrour Zoghi

    Online ranker evaluation is one of the key challenges in information retrieval. Although the preferences of rankers can be inferred by interleaving methods, the problem of how to effectively choose the ranker pair that generates the interleaved list without degrading the user experience too much is still challenging. On the one hand, if two rankers have not been compared enough, the inferred preference

  • Block-Aware Item Similarity Models for Top-NRecommendation
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-09-10
    Yifan Chen; Yang Wang; Xiang Zhao; Jie Zou; Maarten De Rijke

    Top-N recommendations have been studied extensively. Promising results have been achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in the estimation of item similarities. Observing the block-diagonal structure of the item similarities in practice, we propose a block-diagonal regularization (BDR) over item similarities for ICF. The intuitions behind BDR are as

  • Graph-based Regularization on Embedding Layers for Recommendation
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-09-05
    Yuan Zhang; Fei Sun; Xiaoyong Yang; Chen Xu; Wenwu Ou; Yan Zhang

    Neural networks have been extensively used in recommender systems. Embedding layers are not only necessary but also crucial for neural models in recommendation as a typical discrete task. In this article, we argue that the widely used l2 regularization for normal neural layers (e.g., fully connected layers) is not ideal for embedding layers from the perspective of regularization theory in Reproducing

  • End-to-End Neural Matching for Semantic Location Prediction of Tweets
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-09-05
    Paul Mousset; Yoann Pitarch; Lynda Tamine

    The impressive increasing availability of social media posts has given rise to considerable research challenges. This article is concerned with the problem of semantic location prediction of geotagged tweets. The underlying task is to associate to a social media post, the focal spatial object, if any (e.g., Place Of Interest POI), it topically focuses on. Although relevant for a number of applications

  • Pairwise Link Prediction Model for Out of Vocabulary Knowledge Base Entities
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-09-02
    Richong Zhang; Samuel Mensah; Fanshuang Kong; Zhiyuan Hu; Yongyi Mao; Xudong Liu

    Real-world knowledge bases such as DBPedia, Yago, and Freebase contain sparse linkage connectivity, which poses a severe challenge to link prediction between entities. To cope with such data scarcity issues, recent models have focused on learning interactions between entity pairs by means of relations that exist between them. However promising, some relations are associated with very few tail entities

  • Large-Alphabet Semi-Static Entropy Coding Via Asymmetric Numeral Systems
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-07-21
    Alistair Moffat; Matthias Petri

    An entropy coder takes as input a sequence of symbol identifiers over some specified alphabet and represents that sequence as a bitstring using as few bits as possible, typically assuming that the elements of the sequence are independent of each other. Previous entropy coding methods include the well-known Huffman and arithmetic approaches. Here we examine the newer asymmetric numeral systems (ANS)

  • CRSAL: Conversational Recommender Systems with Adversarial Learning
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-06-13
    Xuhui Ren; Hongzhi Yin; Tong Chen; Hao Wang; Nguyen Quoc Viet Hung; Zi Huang; Xiangliang Zhang

    Recommender systems have been attracting much attention from both academia and industry because of their ability to capture user interests and generate personalized item recommendations. As the life pace in contemporary society speeds up, traditional recommender systems are inevitably limited by their disconnected interaction styles and low adaptivity to users’ evolving demands. Consequently, conversational

  • A Survey on Heterogeneous One-class Collaborative Filtering
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-08-11
    Xiancong Chen; Lin Li; Weike Pan; Zhong Ming

    Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users’ feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users’ feedback are usually heterogeneous (rather than homogeneous) such as purchases

  • Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-05-21
    Ruihong Qiu; Zi Huang; Jingjing Li; Hongzhi Yin

    Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user’s recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within individual sessions, which are not expressive

  • OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-04-21
    Wyverson Bonasoli de Oliveira; Leyza Baldo Dorini; Rodrigo Minetto; Thiago H. Silva

    Opinion mining in outdoor images posted by users during different activities can provide valuable information to better understand urban areas. In this regard, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures and one specifically designed for sentiment analysis. We also evaluate

  • Safe Exploration for Optimizing Contextual Bandits
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-04-21
    Rolf Jagerman; Ilya Markov; Maarten De Rijke

    Contextual bandit problems are a natural fit for many information retrieval tasks, such as learning to rank, text classification, recommendation, and so on. However, existing learning methods for contextual bandit problems have one of two drawbacks: They either do not explore the space of all possible document rankings (i.e., actions) and, thus, may miss the optimal ranking, or they present suboptimal

  • FNED: A Deep Network for Fake News Early Detection on Social Media
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-05-05
    Yang Liu; Yi-Fang Brook Wu

    The fast spreading of fake news stories on social media can cause inestimable social harm. Developing effective methods to detect them early is of paramount importance. A major challenge of fake news early detection is fully utilizing the limited data observed at the early stage of news propagation and then learning useful patterns from it for identifying fake news. In this article, we propose a novel

  • Keeping the Data Lake in Form: Proximity Mining for Pre-Filtering Schema Matching
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-05-13
    Ayman Alserafi; Alberto Abelló; Oscar Romero; Toon Calders

    Data lakes (DLs) are large repositories of raw datasets from disparate sources. As more datasets are ingested into a DL, there is an increasing need for efficient techniques to profile them and to detect the relationships among their schemata, commonly known as holistic schema matching. Schema matching detects similarity between the information stored in the datasets to support information discovery

  • Towards Question-based High-recall Information Retrieval: Locating the Last Few Relevant Documents for Technology-assisted Reviews
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-05-13
    Jie Zou; Evangelos Kanoulas

    While continuous active learning algorithms have proven effective in finding most of the relevant documents in a collection, the cost for locating the last few remains high for applications such as Technology-assisted Reviews (TAR). To locate these last few but significant documents efficiently, Zou et al. [2018] have proposed a novel interactive algorithm. The algorithm is based on constructing questions

  • Nonuniform Hyper-Network Embedding with Dual Mechanism
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-05-05
    Jie Huang; Chuan Chen; Fanghua Ye; Weibo Hu; Zibin Zheng

    Network embedding which aims to learn the low-dimensional representations for vertices in networks has been extensively studied in recent years. Although there are various models designed for networks with different properties and different structures for different tasks, most of them are only applied to normal networks which only contain pairwise relationships between vertices. In many realistic cases

  • Using an Inverted Index Synopsis for Query Latency and Performance Prediction
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-05-13
    Nicola Tonellotto; Craig Macdonald

    Predicting the query latency by a search engine has important benefits, for instance, in allowing the search engine to adjust its configuration to address long-running queries without unnecessarily sacrificing its effectiveness. However, for the dynamic pruning techniques that underlie many commercial search engines, achieving accurate predictions of query latencies is difficult. We propose the use

  • Robust Unsupervised Cross-modal Hashing for Multimedia Retrieval
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-06-05
    Miaomiao Cheng; Liping Jing; Michael K. Ng

    With the quick development of social websites, there are more opportunities to have different media types (such as text, image, video, etc.) describing the same topic from large-scale heterogeneous data sources. To efficiently identify the inter-media correlations for multimedia retrieval, unsupervised cross-modal hashing (UCMH) has gained increased interest due to the significant reduction in computation

  • Dual-factor Generation Model for Conversation
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-06-05
    Ruqing Zhang; Jiafeng Guo; Yixing Fan; Yanyan Lan; Xueqi Cheng

    The conversation task is usually formulated as a conditional generation problem, i.e., to generate a natural and meaningful response given the input utterance. Generally speaking, this formulation is apparently based on an oversimplified assumption that the response is solely dependent on the input utterance. It ignores the subjective factor of the responder, e.g., his/her emotion or knowledge state

  • Enhancing Employer Brand Evaluation with Collaborative Topic Regression Models
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-05-22
    Hao Lin; Hengshu Zhu; Junjie Wu; Yuan Zuo; Chen Zhu; Hui Xiong

    Employer Brand Evaluation (EBE) is to understand an employer’s unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers’ financial information, including financial reports and filings submitted to the Securities and Exchange Commission (SEC), which may not be readily available for private companies. Fortunately, online recruitment services provide a variety

  • Challenges in Building Intelligent Open-domain Dialog Systems
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-04-09
    Minlie Huang; Xiaoyan Zhu; Jianfeng Gao

    There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI [33]. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and

  • Special Issue Proposal: Conversational Search and Recommendation
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-02-04
    Claudia Hauff; Julia Kiseleva; Mark Sanderson; Hamed Zamani; Yongfeng Zhang

    The rapid growth in speech and small screen interfaces, particularly on mobile devices, has significantly influenced the way users interact with intelligent systems to satisfy their information needs. The growing interest in personal digital assistants, such as Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana, demonstrates the willingness of users to employ conversational interactions

  • Using Replicates in Information Retrieval Evaluation.
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2018-06-16
    Ellen M Voorhees,Daniel Samarov,Ian Soboroff

    This article explores a method for more accurately estimating the main effect of the system in a typical test-collection-based evaluation of information retrieval systems, thus increasing the sensitivity of system comparisons. Randomly partitioning the test document collection allows for multiple tests of a given system and topic (replicates). Bootstrap ANOVA can use these replicates to extract system-topic

  • Yum-Me: A Personalized Nutrient-Based Meal Recommender System.
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2017-08-01
    Longqi Yang,Cheng-Kang Hsieh,Hongjian Yang,John P Pollak,Nicola Dell,Serge Belongie,Curtis Cole,Deborah Estrin

    Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period

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