当前期刊: arXiv - CS - Information Retrieval Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
我的关注
我的收藏
您暂时未登录!
登录
  • BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer
    arXiv.cs.IR Pub Date : 2020-06-30
    N. Nikzad-Khasmakhi; M. A. Balafar; M. Reza Feizi-Derakhshi; Cina Motamed

    The objective of an expert recommendation system is to trace a set of candidates' expertise and preferences, recognize their expertise patterns, and identify experts. In this paper, we introduce a multimodal classification approach for expert recommendation system (BERTERS). In our proposed system, the modalities are derived from text (articles published by candidates) and graph (their co-author connections)

    更新日期:2020-07-15
  • AutoRec: An Automated Recommender System
    arXiv.cs.IR Pub Date : 2020-06-26
    Ting-Hsiang Wang; Qingquan Song; Xiaotian Han; Zirui Liu; Haifeng Jin; Xia Hu

    Realistic recommender systems are often required to adapt to ever-changing data and tasks or to explore different models systematically. To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation

    更新日期:2020-07-15
  • Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting
    arXiv.cs.IR Pub Date : 2020-07-01
    Longbing Cao

    While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems

    更新日期:2020-07-15
  • Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning
    arXiv.cs.IR Pub Date : 2020-06-28
    Wenhui Yu; Zheng Qin

    Implicit feedback data is extensively explored in recommendation as it is easy to collect and generally applicable. However, predicting users' preference on implicit feedback data is a challenging task since we can only observe positive (voted) samples and unvoted samples. It is difficult to distinguish between the negative samples and unlabeled positive samples from the unvoted ones. Existing works

    更新日期:2020-07-15
  • Deep Retrieval: An End-to-End Learnable Structure Model for Large-Scale Recommendations
    arXiv.cs.IR Pub Date : 2020-07-12
    Weihao Gao; Xiangjun Fan; Jiankai Sun; Kai Jia; Wenzhi Xiao; Chong Wang; Xiaobing Liu

    One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model and then use maximum inner product search (MIPS) algorithms to search top candidates, leading to potential loss of retrieval accuracy. In this paper,

    更新日期:2020-07-15
  • A Framework for Capturing and Analyzing Unstructured and Semi-structured Data for a Knowledge Management System
    arXiv.cs.IR Pub Date : 2020-07-14
    Gerald Onwujekwe; Kweku-Muata Osei-Bryson; Nnatubemugo Ngwum

    Mainstream knowledge management researchers generally agree that knowledge extracted from unstructured data and semi-structured data have become imperative for organizational strategic decision making. In this research, we develop a framework that captures and analyses unstructured data using machine learning techniques and integrates knowledge and insight gained from the data into traditional knowledge

    更新日期:2020-07-15
  • Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
    arXiv.cs.IR Pub Date : 2020-06-28
    Wenhui Yu; Xiao Lin; Junfeng Ge; Wenwu Ou; Zheng Qin

    Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples in the implicit feedbacks and it is a common

    更新日期:2020-07-15
  • MRIF: Multi-resolution Interest Fusion for Recommendation
    arXiv.cs.IR Pub Date : 2020-07-08
    Shihao LiAlibaba Inc; Dekun YangAlibaba Inc; Bufeng ZhangAlibaba Inc

    The main task of personalized recommendation is capturing users' interests based on their historical behaviors. Most of recent advances in recommender systems mainly focus on modeling users' preferences accurately using deep learning based approaches. There are two important properties of users' interests, one is that users' interests are dynamic and evolve over time, the other is that users' interests

    更新日期:2020-07-15
  • Unsupervised Data Extraction from Computer-generated Documents with Single Line Formatting
    arXiv.cs.IR Pub Date : 2020-07-07
    Vladimir Bernstein; Andrei Afanassenkov

    Processing large amounts of data is an essential problem of the big data era. Most of the data exchange is done via direct communication (using APIs) and well-structured file formats (JSON, XML, EDI, etc.), but a significant portion of the data is transferred using arbitrary formatted computer-generated documents (such as invoices, purchase orders, financial reports, etc.), which require sophisticated

    更新日期:2020-07-15
  • Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation
    arXiv.cs.IR Pub Date : 2020-07-11
    Ilia Kravets; Tal Heletz; Hayit Greenspan

    Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history. We present a deep learning system that transforms a 3D image of a pulmonary nodule from a CT scan into a low-dimensional embedding vector. We demonstrate that such a vector representation

    更新日期:2020-07-15
  • Template-Based Question Answering over Linked Geospatial Data
    arXiv.cs.IR Pub Date : 2020-07-14
    Dharmen Punjani; Markos Iliakis; Theodoros Stefou; Kuldeep Singh; Andreas Both; Manolis Koubarakis; Iosif Angelidis; Konstantina Bereta; Themis Beris; Dimitris Bilidas; Theofilos Ioannidis; Nikolaos Karalis; Christoph Lange; Despina-Athanasia Pantazi; Christos Papaloukas; Georgios Stamoulis

    Large amounts of geospatial data have been made available recently on the linked open data cloud and the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper

    更新日期:2020-07-15
  • Feedback Clustering for Online Travel Agencies Searches: a Case Study
    arXiv.cs.IR Pub Date : 2020-06-28
    Sara Scaramuccia; Simon Nanty; Florent Masseglia

    Understanding choices performed by online customers is a growing need in the travel industry. In many practical situations, the only available information is the flight search query performed by the customer with no additional profile knowledge. In general, customer flight bookings are driven by prices, duration, number of connections, and so on. However, not all customers might assign the same importance

    更新日期:2020-07-15
  • Recommender Systems for the Internet of Things: A Survey
    arXiv.cs.IR Pub Date : 2020-07-14
    May Altulyan; Lina Yao; Xianzhi Wang; Chaoran Huang; Salil S Kanhere; Quan Z Sheng

    Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT. We discuss several limitations

    更新日期:2020-07-15
  • A supervised term-weighting technique for topic-based retrieval
    arXiv.cs.IR Pub Date : 2020-07-13
    Mariano Maisonnave; Fernando Delbianco; Fernando Tohmé; Ana Maguitman

    This article presents a technique for term weighting that relies on a collection of documents labeled as relevant or irrelevant to a topic of interest. The proposed technique weights terms based on two factors representing the descriptive and discriminating power of the terms. These factors are combined through the use of an adjustable parameter into a general measure that allows for the selection

    更新日期:2020-07-15
  • Conditional Image Retrieval
    arXiv.cs.IR Pub Date : 2020-07-14
    Mark Hamilton; Stephanie Fu; William T. Freeman; Mindren Lu

    This work introduces Conditional Image Retrieval (CIR) systems: IR methods that can efficiently specialize to specific subsets of images on the fly. These systems broaden the class of queries IR systems support, and eliminate the need for expensive re-fitting to specific subsets of data. Specifically, we adapt tree-based K-Nearest Neighbor (KNN) data-structures to the conditional setting by introducing

    更新日期:2020-07-15
  • A $\texttt{SUPER}^{\ast}$ Algorithm to Optimize Paper Bidding in Peer Review
    arXiv.cs.IR Pub Date : 2020-06-27
    Tanner Fiez; Nihar B. Shah; Lillian Ratliff

    A number of applications involve sequential arrival of users, and require showing each user an ordering of items. A prime example (which forms the focus of this paper) is the bidding process in conference peer review where reviewers enter the system sequentially, each reviewer needs to be shown the list of submitted papers, and the reviewer then "bids" to review some papers. The order of the papers

    更新日期:2020-07-15
  • COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes
    arXiv.cs.IR Pub Date : 2020-07-14
    Raj Kumar Gupta; Ajay Vishwanath; Yinping Yang

    This resource paper describes a large dataset covering over 63 million coronavirus-related Twitter posts from more than 13 million unique users since 28 January to 1 July 2020. As strong concerns and emotions are expressed in the tweets, we analyzed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes

    更新日期:2020-07-15
  • Learning Choice Functions via Pareto-Embeddings
    arXiv.cs.IR Pub Date : 2020-07-14
    Karlson Pfannschmidt; Eyke Hüllermeier

    We consider the problem of learning to choose from a given set of objects, where each object is represented by a feature vector. Traditional approaches in choice modelling are mainly based on learning a latent, real-valued utility function, thereby inducing a linear order on choice alternatives. While this approach is suitable for discrete (top-1) choices, it is not straightforward how to use it for

    更新日期:2020-07-15
  • Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
    arXiv.cs.IR Pub Date : 2020-06-29
    Qingquan Song; Dehua Cheng; Hanning Zhou; Jiyan Yang; Yuandong Tian; Xia Hu

    Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural architecture search (NAS), as an emerging field, has demonstrated its capabilities in discovering powerful neural network architectures, which motivates us to explore its potential for CTR predictions. Due to 1) diverse unstructured

    更新日期:2020-07-14
  • A Feature Analysis for Multimodal News Retrieval
    arXiv.cs.IR Pub Date : 2020-07-13
    Golsa Tahmasebzadeh; Sherzod Hakimov; Eric Müller-Budack; Ralph Ewerth

    Content-based information retrieval is based on the information contained in documents rather than using metadata such as keywords. Most information retrieval methods are either based on text or image. In this paper, we investigate the usefulness of multimodal features for cross-lingual news search in various domains: politics, health, environment, sport, and finance. To this end, we consider five

    更新日期:2020-07-14
  • Graph Factorization Machines for Cross-Domain Recommendation
    arXiv.cs.IR Pub Date : 2020-07-12
    Dongbo Xi; Fuzhen Zhuang; Yongchun Zhu; Pengpeng Zhao; Xiangliang Zhang; Qing He

    Recently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result of multiple factors acting at the same time. However, a long-standing challenge is how to effectively aggregate multi-order interactions in GNN. In this paper, we propose a Graph Factorization Machine (GFM) which utilizes

    更新日期:2020-07-14
  • Indias rank and proportionate share in the global research output part 2 how publication counting method and subject selection can vary the outcomes
    arXiv.cs.IR Pub Date : 2020-07-12
    Vivek Kumar Singh; Parveen Arora; Ashraf Uddin; Sujit Bhattacharya

    During the last two decades, India has emerged as a major knowledge producer in the world, however different reports put it at different ranks, varying from 3rd to 9th places. The recent commissioned study reports of Department of Science and Technology (DST) done by Elsevier and Clarivate Analytics, rank India at 5thand 9th places, respectively. On the other hand, an independent report by National

    更新日期:2020-07-14
  • Indias rank and proportionate share in the global research output part 1 how data sourced from different databases can produce different outcomes
    arXiv.cs.IR Pub Date : 2020-07-12
    Prashasti Singh; Vivek Kumar Singh; Parveen Arora; Sujit Bhattacharya

    India is emerging as a major knowledge producer of the world in terms of proportionate share of global research output and the overall research productivity rank. Many recent reports, both of commissioned studies from Government of India as well as independent international agencies, show India at different ranks of global research productivity (variations as large as from 3rd to 9th place). The paper

    更新日期:2020-07-14
  • HyperGrid: Efficient Multi-Task Transformers with Grid-wise Decomposable Hyper Projections
    arXiv.cs.IR Pub Date : 2020-07-12
    Yi Tay; Zhe Zhao; Dara Bahri; Donald Metzler; Da-Cheng Juan

    Achieving state-of-the-art performance on natural language understanding tasks typically relies on fine-tuning a fresh model for every task. Consequently, this approach leads to a higher overall parameter cost, along with higher technical maintenance for serving multiple models. Learning a single multi-task model that is able to do well for all the tasks has been a challenging and yet attractive proposition

    更新日期:2020-07-14
  • BISON:BM25-weighted Self-Attention Framework for Multi-Fields Document Search
    arXiv.cs.IR Pub Date : 2020-07-10
    Xuan Shan; Chuanjie Liu; Yiqian Xia; Qi Chen; Yusi Zhang; Angen Luo; Yuxiang Luo

    Recent breakthrough in natural language processing has advanced the information retrieval from keyword match to semantic vector search. To map query and documents into semantic vectors, self-attention models are being widely used. However, typical self-attention models, like Transformer, lack prior knowledge to distinguish the importance of different tokens, which has been proved to play a critical

    更新日期:2020-07-13
  • Topic Modeling on User Stories using Word Mover's Distance
    arXiv.cs.IR Pub Date : 2020-07-10
    Kim Julian Gülle; Nicholas Ford; Patrick Ebel; Florian Brokhausen; Andreas Vogelsang

    Requirements elicitation has recently been complemented with crowd-based techniques, which continuously involve large, heterogeneous groups of users who express their feedback through a variety of media. Crowd-based elicitation has great potential for engaging with (potential) users early on but also results in large sets of raw and unstructured feedback. Consolidating and analyzing this feedback is

    更新日期:2020-07-13
  • Handling Collocations in Hierarchical Latent Tree Analysis for Topic Modeling
    arXiv.cs.IR Pub Date : 2020-07-10
    Leonard K. M. Poon; Nevin L. Zhang; Haoran Xie; Gary Cheng

    Topic modeling has been one of the most active research areas in machine learning in recent years. Hierarchical latent tree analysis (HLTA) has been recently proposed for hierarchical topic modeling and has shown superior performance over state-of-the-art methods. However, the models used in HLTA have a tree structure and cannot represent the different meanings of multiword expressions sharing the

    更新日期:2020-07-13
  • On the Social and Technical Challenges of Web Search Autosuggestion Moderation
    arXiv.cs.IR Pub Date : 2020-07-09
    Timothy J. Hazen; Alexandra Olteanu; Gabriella Kazai; Fernando Diaz; Michael Golebiewski

    Past research shows that users benefit from systems that support them in their writing and exploration tasks. The autosuggestion feature of Web search engines is an example of such a system: It helps users in formulating their queries by offering a list of suggestions as they type. Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document

    更新日期:2020-07-13
  • Inductive Relational Matrix Completion
    arXiv.cs.IR Pub Date : 2020-07-09
    Qitian Wu; Hengrui Zhang; Hongyuan Zha

    Data sparsity and cold-start issues emerge as two major bottlenecks for matrix completion in the context of user-item interaction matrix. We propose a novel method that can fundamentally address these issues. The main idea is to partition users into support users, which have many observed interactions (i.e., non-zero entries in the matrix), and query users, which have few observed entries. For support

    更新日期:2020-07-10
  • A Systematic Review on Context-Aware Recommender Systems using Deep Learning and Embeddings
    arXiv.cs.IR Pub Date : 2020-07-09
    Igor André Pegoraro Santana; Marcos Aurelio Domingues

    Recommender Systems are tools that improve how users find relevant information in web systems, so they do not face too much information. In order to generate better recommendations, the context of information should be used in the recommendation process. Context-Aware Recommender Systems were created, accomplishing state-of-the-art results and improving traditional recommender systems. There are many

    更新日期:2020-07-10
  • Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers
    arXiv.cs.IR Pub Date : 2020-07-09
    Jimin Wang; Yingjie Hu

    A rich amount of geographic information exists in unstructured texts, such as Web pages, social media posts, housing advertisements, and historical archives. Geoparsers are useful tools that extract structured geographic information from unstructured texts, thereby enabling spatial analysis on textual data. While a number of geoparsers were developed, they were tested on different datasets using different

    更新日期:2020-07-10
  • A Survey of Quantum Theory Inspired Approaches to Information Retrieval
    arXiv.cs.IR Pub Date : 2020-07-08
    Sagar Uprety; Dimitris Gkoumas; Dawei Song

    Since 2004, researchers have been using the mathematical framework of Quantum Theory (QT) in Information Retrieval (IR). QT offers a generalized probability and logic framework. Such a framework has been shown capable of unifying the representation, ranking and user cognitive aspects of IR, and helpful in developing more dynamic, adaptive and context-aware IR systems. Although Quantum-inspired IR is

    更新日期:2020-07-10
  • Open Data Quality Evaluation: A Comparative Analysis of Open Data in Latvia
    arXiv.cs.IR Pub Date : 2020-07-09
    Anastasija Nikiforova

    Nowadays open data is entering the mainstream - it is free available for every stakeholder and is often used in business decision-making. It is important to be sure data is trustable and error-free as its quality problems can lead to huge losses. The research discusses how (open) data quality could be assessed. It also covers main points which should be considered developing a data quality management

    更新日期:2020-07-10
  • Less is More: Rejecting Unreliable Reviews for Product Question Answering
    arXiv.cs.IR Pub Date : 2020-07-09
    Shiwei Zhang; Xiuzhen Zhang; Jey Han Lau; Jeffrey Chan; Cecile Paris

    Promptly and accurately answering questions on products is important for e-commerce applications. Manually answering product questions (e.g. on community question answering platforms) results in slow response and does not scale. Recent studies show that product reviews are a good source for real-time, automatic product question answering (PQA). In the literature, PQA is formulated as a retrieval problem

    更新日期:2020-07-10
  • Cooking Is All About People: Comment Classification On Cookery Channels Using BERT and Classification Models (Malayalam-English Mix-Code)
    arXiv.cs.IR Pub Date : 2020-06-15
    Subramaniam KazhuparambilDublin Business School; Abhishek KaushikDublin Business SchoolDublin City University

    The scope of a lucrative career promoted by Google through its video distribution platform YouTube has attracted a large number of users to become content creators. An important aspect of this line of work is the feedback received in the form of comments which show how well the content is being received by the audience. However, volume of comments coupled with spam and limited tools for comment classification

    更新日期:2020-07-09
  • Agile Approach for IT Forensics Management
    arXiv.cs.IR Pub Date : 2020-07-08
    Matthias Schopp; Peter Hillmann

    The forensic investigation of cyber attacks and IT incidents is becoming increasingly difficult due to increasing complexity and intensify networking. Especially with Advanced Attacks (AT) like the increasing Advanced Persistent Threats an agile approach is indispensable. Several systems are involved in an attack (multi-host attacks). Current forensic models and procedures show considerable deficits

    更新日期:2020-07-09
  • Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
    arXiv.cs.IR Pub Date : 2020-07-08
    Kun Zhou; Wayne Xin Zhao; Shuqing Bian; Yuanhang Zhou; Ji-Rong Wen; Jingsong Yu

    Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression

    更新日期:2020-07-09
  • Unbiased Lift-based Bidding System
    arXiv.cs.IR Pub Date : 2020-07-08
    Daisuke Moriwaki; Yuta Hayakawa; Isshu Munemasa; Yuta Saito; Akira Matsui

    Conventional bidding strategies for online display ad auction heavily relies on observed performance indicators such as clicks or conversions. A bidding strategy naively pursuing these easily observable metrics, however, fails to optimize the profitability of the advertisers. Rather, the bidding strategy that leads to the maximum revenue is a strategy pursuing the performance \textit{lift} of showing

    更新日期:2020-07-09
  • The impact of political party/candidate on the election results from a sentiment analysis perspective using #AnambraDecides2017 tweets
    arXiv.cs.IR Pub Date : 2020-07-07
    Ikechukwu Onyenwe; Samuel Nwagbo; Njideka Mbeledogu; Ebele Onyedinma

    This work investigates empirically the impact of political party control over its candidates or vice versa on winning an election using a natural language processing technique called sentiment analysis (SA). To do this, a set of 7430 tweets bearing or related to #AnambraDecides2017 was streamed during the November 18, 2017, Anambra State gubernatorial election. These are Twitter discussions on the

    更新日期:2020-07-09
  • ISA: An Intelligent Shopping Assistant
    arXiv.cs.IR Pub Date : 2020-07-07
    Tuan Manh Lai; Trung Bui; Nedim Lipka

    Despite the growth of e-commerce, brick-and-mortar stores are still the preferred destinations for many people. In this paper, we present ISA, a mobile-based intelligent shopping assistant that is designed to improve shopping experience in physical stores. ISA assists users by leveraging advanced techniques in computer vision, speech processing, and natural language processing. An in-store user only

    更新日期:2020-07-09
  • Placepedia: Comprehensive Place Understanding with Multi-Faceted Annotations
    arXiv.cs.IR Pub Date : 2020-07-07
    Huaiyi Huang; Yuqi Zhang; Qingqiu Huang; Zhengkui Guo; Ziwei Liu; Dahua Lin

    Place is an important element in visual understanding. Given a photo of a building, people can often tell its functionality, e.g. a restaurant or a shop, its cultural style, e.g. Asian or European, as well as its economic type, e.g. industry oriented or tourism oriented. While place recognition has been widely studied in previous work, there remains a long way towards comprehensive place understanding

    更新日期:2020-07-09
  • Cross-lingual Inductive Transfer to Detect Offensive Language
    arXiv.cs.IR Pub Date : 2020-07-07
    Kartikey Pant; Tanvi Dadu

    With the growing use of social media and its availability, many instances of the use of offensive language have been observed across multiple languages and domains. This phenomenon has given rise to the growing need to detect the offensive language used in social media cross-lingually. In OffensEval 2020, the organizers have released the \textit{multilingual Offensive Language Identification Dataset}

    更新日期:2020-07-09
  • Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case Document Similarity
    arXiv.cs.IR Pub Date : 2020-07-07
    Paheli Bhattacharya; Kripabandhu Ghosh; Arindam Pal; Saptarshi Ghosh

    Computing similarity between two legal case documents is an important and challenging task in Legal IR, for which text-based and network-based measures have been proposed in literature. All prior network-based similarity methods considered a precedent citation network among case documents only (PCNet). However, this approach misses an important source of legal knowledge -- the hierarchy of legal statutes

    更新日期:2020-07-08
  • MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
    arXiv.cs.IR Pub Date : 2020-07-07
    Manqing Dong; Feng Yuan; Lina Yao; Xiwei Xu; Liming Zhu

    A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handle situations with new users or new items. Recently, some works introduce the meta-optimization idea into the recommendation scenarios, i.e. predicting the user preference by only a few of past interacted items. The core idea

    更新日期:2020-07-08
  • Predicting Afrobeats Hit Songs Using Spotify Data
    arXiv.cs.IR Pub Date : 2020-07-07
    Adewale Adeagbo

    This study approached the Hit Song Science problem with the aim of predicting which songs in the Afrobeats genre will become popular among Spotify listeners. A dataset of 2063 songs was generated through the Spotify Web API, with the provided audio features. Random Forest and Gradient Boosting algorithms proved to be successful with approximately F1 scores of 86%.

    更新日期:2020-07-08
  • An Evaluation of Publicly Available Deep Learning Based Commercial Information Retrieval Systems to search Biomedical Articles related to COVID-19
    arXiv.cs.IR Pub Date : 2020-07-06
    Sarvesh Soni; Kirk Roberts

    The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, primarily through the use of text mining and search tools. This has led to both corpora for biomedical articles related to COVID-19 (such as the CORD-19 corpus (Wang et al., 2020)) as well as search engines to query such data. While most research in search engines is performed in the academic field

    更新日期:2020-07-08
  • PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest
    arXiv.cs.IR Pub Date : 2020-07-07
    Aditya Pal; Chantat Eksombatchai; Yitong Zhou; Bo Zhao; Charles Rosenberg; Jure Leskovec

    Latent user representations are widely adopted in the tech industry for powering personalized recommender systems. Most prior work infers a single high dimensional embedding to represent a user, which is a good starting point but falls short in delivering a full understanding of the user's interests. In this work, we introduce PinnerSage, an end-to-end recommender system that represents each user via

    更新日期:2020-07-08
  • On the Efficiency of Decentralized File Storage for Personal Information Management Systems
    arXiv.cs.IR Pub Date : 2020-07-07
    Mirko Zichichi; Stefano Ferretti; Gabriele D'Angelo

    This paper presents an architecture, based on Distributed Ledger Technologies (DLTs) and Decentralized File Storage (DFS) systems, to support the use of Personal Information Management Systems (PIMS). DLT and DFS are used to manage data sensed by mobile users equipped with devices with sensing capability. DLTs guarantee the immutability, traceability and verifiability of references to personal data

    更新日期:2020-07-08
  • Deep Contextual Embeddings for Address Classification in E-commerce
    arXiv.cs.IR Pub Date : 2020-07-06
    Shreyas Mangalgi; Lakshya Kumar; Ravindra Babu Tallamraju

    E-commerce customers in developing nations like India tend to follow no fixed format while entering shipping addresses. Parsing such addresses is challenging because of a lack of inherent structure or hierarchy. It is imperative to understand the language of addresses, so that shipments can be routed without delays. In this paper, we propose a novel approach towards understanding customer addresses

    更新日期:2020-07-08
  • Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
    arXiv.cs.IR Pub Date : 2020-07-03
    Hamad Zogan; Xianzhi Wang; Shoaib Jameel; Guandong Xu

    Social networks enable people to interact with one another by sharing information, sending messages, making friends, and having discussions, which generates massive amounts of data every day, popularly called as the user-generated content. This data is present in various forms such as images, text, videos, links, and others and reflects user behaviours including their mental states. It is challenging

    更新日期:2020-07-07
  • GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation
    arXiv.cs.IR Pub Date : 2020-07-06
    Qiu Ruihong; Yin Hongzhi; Huang Zi; Tong Chen

    Streaming session-based recommendation (SSR) is a challenging task that requires the recommender system to do the session-based recommendation (SR) in the streaming scenario. In the real-world applications of e-commerce and social media, a sequence of user-item interactions generated within a certain period are grouped as a session, and these sessions consecutively arrive in the form of streams. Most

    更新日期:2020-07-07
  • Reducing Misinformation in Query Autocompletions
    arXiv.cs.IR Pub Date : 2020-07-06
    Djoerd Hiemstra

    Query autocompletions help users of search engines to speed up their searches by recommending completions of partially typed queries in a drop down box. These recommended query autocompletions are usually based on large logs of queries that were previously entered by the search engine's users. Therefore, misinformation entered -- either accidentally or purposely to manipulate the search engine -- might

    更新日期:2020-07-07
  • Searching Scientific Literature for Answers on COVID-19 Questions
    arXiv.cs.IR Pub Date : 2020-07-06
    Vincent Nguyen; Maciek Rybinski; Sarvnaz Karimi; Zhenchang Xing

    Finding answers related to a pandemic of a novel disease raises new challenges for information seeking and retrieval, as the new information becomes available gradually. TREC COVID search track aims to assist in creating search tools to aid scientists, clinicians, policy makers and others with similar information needs in finding reliable answers from the scientific literature. We experiment with different

    更新日期:2020-07-07
  • Learning Personalized Risk Preferences for Recommendation
    arXiv.cs.IR Pub Date : 2020-07-06
    Yingqiang Ge; Shuyuan Xu; Shuchang Liu; Zuohui Fu; Fei Sun; Yongfeng Zhang

    The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and review information to make purchase decisions. With this information, they can infer the quality of products to reduce the risk of purchase. Specifically, items with high rating scores and good reviews tend to be less risky, while

    更新日期:2020-07-07
  • Understanding Echo Chambers in E-commerce Recommender Systems
    arXiv.cs.IR Pub Date : 2020-07-06
    Yingqiang Ge; Shuya Zhao; Honglu Zhou; Changhua Pei; Fei Sun; Wenwu Ou; Yongfeng Zhang

    Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper items based on user interests. However, significant efforts are missing to understand how the recommendations influence user preferences and behaviors, e.g., if and how recommendations result in \textit{echo chambers}. Extensive

    更新日期:2020-07-07
  • Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media
    arXiv.cs.IR Pub Date : 2020-07-05
    Hui Yin; Shuiqiao Yang; Jianxin Li

    The outbreak of the novel Coronavirus Disease (COVID-19) has greatly influenced people's daily lives across the globe. Emergent measures and policies (e.g., lockdown, social distancing) have been taken by governments to combat this highly infectious disease. However, people's mental health is also at risk due to the long-time strict social isolation rules. Hence, monitoring people's mental health across

    更新日期:2020-07-07
  • Neural Interactive Collaborative Filtering
    arXiv.cs.IR Pub Date : 2020-07-04
    Lixin Zou; Long Xia; Yulong Gu; Xiangyu Zhao; Weidong Liu; Jimmy Xiangji Huang; Dawei Yin

    In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem in this scenario is how to suggest items when the user profile has not been well established, i.e., recommend for cold-start users or warm-start users with taste

    更新日期:2020-07-07
  • Overlaying Spaces and Practical Applicability of Complex Geometries
    arXiv.cs.IR Pub Date : 2020-07-05
    Kirill Shevkunov; Liudmila Prokhorenkova

    Recently, non-Euclidean spaces became popular for embedding structured data. Following hyperbolic and spherical spaces, more general product spaces have been proposed. However, searching for the best configuration of a product space is a resource-intensive procedure, which reduces the practical applicability of the idea. We introduce a novel concept of overlaying spaces that does not have the problem

    更新日期:2020-07-07
  • Building benchmarking frameworks for supporting replicability and reproducibility: spatial and textual analysis as an example
    arXiv.cs.IR Pub Date : 2020-07-04
    Yingjie Hu

    Replicability and reproducibility (R&R) are critical for the long-term prosperity of a scientific discipline. In GIScience, researchers have discussed R&R related to different research topics and problems, such as local spatial statistics, digital earth, and metadata (Fotheringham, 2009; Goodchild, 2012; Anselin et al., 2014). This position paper proposes to further support R&R by building benchmarking

    更新日期:2020-07-07
  • News Sentiment Analysis
    arXiv.cs.IR Pub Date : 2020-07-05
    Antony Samuels; John Mcgonical

    Modern technological era has reshaped traditional lifestyle in several domains. The medium of publishing news and events has become faster with the advancement of Information Technology. IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging

    更新日期:2020-07-07
Contents have been reproduced by permission of the publishers.
导出
全部期刊列表>>
产业、创新与基础设施
AI核心技术
10years
自然科研线上培训服务
材料学研究精选
Springer Nature Live 产业与创新线上学术论坛
胸腔和胸部成像专题
自然科研论文编辑服务
ACS ES&T Engineering
ACS ES&T Water
屿渡论文,编辑服务
杨超勇
周一歌
华东师范大学
段炼
清华大学
廖矿标
李远
跟Nature、Science文章学绘图
隐藏1h前已浏览文章
中洪博元
课题组网站
新版X-MOL期刊搜索和高级搜索功能介绍
ACS材料视界
x-mol收录
福州大学
南京大学
王杰
丘龙斌
电子显微学
何凤
洛杉矶分校
吴杰
赵延川
试剂库存
天合科研
down
wechat
bug