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  • 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

    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

  • 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

  • Large-Alphabet Semi-Static Entropy Coding Via Asymmetric Numeral Systems
    ACM Trans. Inf. Syst. (IF 2.889) Pub Date : 2020-04-24
    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 system (ANS)

  • 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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