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  • Automatic Extraction of Agriculture Terms from Domain Text: A Survey of Tools and Techniques
    arXiv.cs.IR Pub Date : 2020-09-24
    Niladri Chatterjee; Neha Kaushik

    Agriculture is a key component in any country's development. Domain-specific knowledge resources serve to gain insight into the domain. Existing knowledge resources such as AGROVOC and NAL Thesaurus are developed and maintained by the domain experts. Population of terms into these knowledge resources can be automated by using automatic term extraction tools for processing unstructured agricultural

    更新日期:2020-09-25
  • Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning
    arXiv.cs.IR Pub Date : 2020-09-24
    Oleksii Moskalenko; Denis Parra; Diego Saez-Trumper

    Wikipedia is edited by volunteer editors around the world. Considering the large amount of existing content (e.g. over 5M articles in English Wikipedia), deciding what to edit next can be difficult, both for experienced users that usually have a huge backlog of articles to prioritize, as well as for newcomers who that might need guidance in selecting the next article to contribute. Therefore, helping

    更新日期:2020-09-25
  • ArXivDigest: A Living Lab for Personalized Scientific Literature Recommendation
    arXiv.cs.IR Pub Date : 2020-09-24
    Kristian Gingstad; Øyvind Jekteberg; Krisztian Balog

    Providing personalized recommendations that are also accompanied by explanations as to why an item is recommended is a research area of growing importance. At the same time, progress is limited by the availability of open evaluation resources. In this work, we address the task of scientific literature recommendation. We present arXivDigest, which is an online service providing personalized arXiv recommendations

    更新日期:2020-09-25
  • Dynamic Similarity Search on Integer Sketches
    arXiv.cs.IR Pub Date : 2020-09-24
    Shunsuke Kanda; Yasuo Tabei

    Similarity-preserving hashing is a core technique for fast similarity searches, and it randomly maps data points in a metric space to strings of discrete symbols (i.e., sketches) in the Hamming space. While traditional hashing techniques produce binary sketches, recent ones produce integer sketches for preserving various similarity measures. However, most similarity search methods are designed for

    更新日期:2020-09-25
  • ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)
    arXiv.cs.IR Pub Date : 2020-09-23
    Mohammad Aliannejadi; Julia Kiseleva; Aleksandr Chuklin; Jeff Dalton; Mikhail Burtsev

    This document presents a detailed description of the challenge on clarifying questions for dialogue systems (ClariQ). The challenge is organized as part of the Conversational AI challenge series (ConvAI3) at Search Oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user

    更新日期:2020-09-25
  • Using the Hammer Only on Nails: A Hybrid Method for Evidence Retrieval for Question Answering
    arXiv.cs.IR Pub Date : 2020-09-22
    Zhengzhong Liang; Yiyun Zhao; Mihai Surdeanu

    Evidence retrieval is a key component of explainable question answering (QA). We argue that, despite recent progress, transformer network-based approaches such as universal sentence encoder (USE-QA) do not always outperform traditional information retrieval (IR) methods such as BM25 for evidence retrieval for QA. We introduce a lexical probing task that validates this observation: we demonstrate that

    更新日期:2020-09-24
  • A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions
    arXiv.cs.IR Pub Date : 2020-09-23
    Florian Wirthmüller; Marvin Klimke; Julian Schlechtriemen; Jochen Hipp; Manfred Reichert

    Already today, driver assistance systems help to make daily traffic more comfortable and safer. However, there are still situations that are quite rare but are hard to handle at the same time. In order to cope with these situations and to bridge the gap towards fully automated driving, it becomes necessary to not only collect enormous amounts of data but rather the right ones. This data can be used

    更新日期:2020-09-24
  • Cosine Similarity of Multimodal Content Vectors for TV Programmes
    arXiv.cs.IR Pub Date : 2020-09-23
    Saba Nazir. Taner Cagali; Chris Newell; Mehrnoosh Sadrzadeh

    Multimodal information originates from a variety of sources: audiovisual files, textual descriptions, and metadata. We show how one can represent the content encoded by each individual source using vectors, how to combine the vectors via middle and late fusion techniques, and how to compute the semantic similarities between the contents. Our vectorial representations are built from spectral features

    更新日期:2020-09-24
  • Towards a Flexible Embedding Learning Framework
    arXiv.cs.IR Pub Date : 2020-09-23
    Chin-Chia Michael Yeh; Dhruv Gelda; Zhongfang Zhuang; Yan Zheng; Liang Gou; Wei Zhang

    Representation learning is a fundamental building block for analyzing entities in a database. While the existing embedding learning methods are effective in various data mining problems, their applicability is often limited because these methods have pre-determined assumptions on the type of semantics captured by the learned embeddings, and the assumptions may not well align with specific downstream

    更新日期:2020-09-24
  • On Data Augmentation for Extreme Multi-label Classification
    arXiv.cs.IR Pub Date : 2020-09-22
    Danqing Zhang; Tao Li; Haiyang Zhang; Bing Yin

    In this paper, we focus on data augmentation for the extreme multi-label classification (XMC) problem. One of the most challenging issues of XMC is the long tail label distribution where even strong models suffer from insufficient supervision. To mitigate such label bias, we propose a simple and effective augmentation framework and a new state-of-the-art classifier. Our augmentation framework takes

    更新日期:2020-09-24
  • Embedding-based Zero-shot Retrieval through Query Generation
    arXiv.cs.IR Pub Date : 2020-09-22
    Davis Liang; Peng Xu; Siamak Shakeri; Cicero Nogueira dos Santos; Ramesh Nallapati; Zhiheng Huang; Bing Xiang

    Passage retrieval addresses the problem of locating relevant passages, usually from a large corpus, given a query. In practice, lexical term-matching algorithms like BM25 are popular choices for retrieval owing to their efficiency. However, term-based matching algorithms often miss relevant passages that have no lexical overlap with the query and cannot be finetuned to downstream datasets. In this

    更新日期:2020-09-23
  • Claraprint: a chord and melody based fingerprint for western classical music cover detection
    arXiv.cs.IR Pub Date : 2020-09-21
    Mickaël Arcos

    Cover song detection has been an active field in the Music Information Retrieval (MIR) community during the past decades. Most of the research community focused in solving it for a wide range of music genres with diverse characteristics. Western classical music, a genre heavily based on the recording of "cover songs", or musical works, represents a large heritage, offering immediate application for

    更新日期:2020-09-23
  • An Exponential Factorization Machine with Percentage Error Minimization to Retail Sales Forecasting
    arXiv.cs.IR Pub Date : 2020-09-22
    Chongshou Li; Brenda Cheang; Zhixing Luo; Andrew Lim

    This paper proposes a new approach to sales forecasting for new products with long lead time but short product life cycle. These SKUs are usually sold for one season only, without any replenishments. An exponential factorization machine (EFM) sales forecast model is developed to solve this problem which not only considers SKU attributes, but also pairwise interactions. The EFM model is significantly

    更新日期:2020-09-23
  • Automating Outlier Detection via Meta-Learning
    arXiv.cs.IR Pub Date : 2020-09-22
    Yue Zhao; Ryan A. Rossi; Leman Akoglu

    Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)? Thus far, model selection for OD has been a "black art"; as any model evaluation is infeasible due to the lack of (i) hold-out data with labels, and (ii) a universal objective function. In this work, we develop the

    更新日期:2020-09-23
  • Beyond Triplet Loss: Person Re-identification with Fine-grained Difference-aware Pairwise Loss
    arXiv.cs.IR Pub Date : 2020-09-22
    Cheng Yan; Guansong Pang; Xiao Bai; Jun Zhou; Lin Gu

    Person Re-IDentification (ReID) aims at re-identifying persons from different viewpoints across multiple cameras. Capturing the fine-grained appearance differences is often the key to accurate person ReID, because many identities can be differentiated only when looking into these fine-grained differences. However, most state-of-the-art person ReID approaches, typically driven by a triplet loss, fail

    更新日期:2020-09-23
  • A Low-Level Index for Distributed Logic Programming
    arXiv.cs.IR Pub Date : 2020-09-22
    Thomas ProkoschInstitute for Informatics, Ludwig-Maximilian University of Munich, Germany

    A distributed logic programming language with support for meta-programming and stream processing offers a variety of interesting research problems, such as: How can a versatile and stable data structure for the indexing of a large number of expressions be implemented with simple low-level data structures? Can low-level programming help to reduce the number of occur checks in Robinson's unification

    更新日期:2020-09-23
  • DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
    arXiv.cs.IR Pub Date : 2020-09-21
    Yujia Zheng; Siyi Liu; Zekun Li; Shu Wu

    The task of session-based recommendation is to predict user actions based on anonymous sessions. Recent research mainly models the target session as a sequence or a graph to capture item transitions within it, ignoring complex transitions between items in different sessions that have been generated by other users. These item transitions include potential collaborative information and reflect similar

    更新日期:2020-09-22
  • "Click" Is Not Equal to "Like": Counterfactual Recommendation for Mitigating Clickbait Issue
    arXiv.cs.IR Pub Date : 2020-09-21
    Wenjie Wang; Fuli Feng; Xiangnan He; Hanwang Zhang; Tat-Seng Chua

    The ubiquity of implicit feedback (e.g., click) makes it the default choice to train recommenders. However, such implicit feedback usually has intrinsic noises and bias that interfere the inference of user preference. For instance, many users click micro-videos due to their attractive titles, and then quickly quit because they dislike the content. The inconsistency between clicks and actual user preference

    更新日期:2020-09-22
  • Field-Embedded Factorization Machines for Click-through rate prediction
    arXiv.cs.IR Pub Date : 2020-09-13
    Harshit Pande

    Click-through rate (CTR) prediction models are common in many online applications such as digital advertising and recommender systems. Field-Aware Factorization Machine (FFM) and Field-weighted Factorization Machine (FwFM) are state-of-the-art among the shallow models for CTR prediction. Recently, many deep learning-based models have also been proposed. Among deeper models, DeepFM, xDeepFM, AutoInt+

    更新日期:2020-09-22
  • AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis
    arXiv.cs.IR Pub Date : 2020-09-13
    Mohammad Abdul Hadi; Fatemeh H Fard

    Analysis of mobile app reviews has shown its important role in requirement engineering, software maintenance and evolution of mobile apps. Mobile app developers check their users' reviews frequently to clarify the issues experienced by users or capture the new issues that are introduced due to a recent app update. App reviews have a dynamic nature and their discussed topics change over time. The changes

    更新日期:2020-09-22
  • Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes
    arXiv.cs.IR Pub Date : 2020-09-11
    Markus Schedl; Christine Bauer; Wolfgang Reisinger; Dominik Kowald; Elisabeth Lex

    Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between

    更新日期:2020-09-22
  • Longformer for MS MARCO Document Re-ranking Task
    arXiv.cs.IR Pub Date : 2020-09-20
    Ivan Sekulić; Amir Soleimani; Mohammad Aliannejadi; Fabio Crestani

    Two step document ranking, where the initial retrieval is done by a classical information retrieval method, followed by neural re-ranking model, is the new standard. The best performance is achieved by using transformer-based models as re-rankers, e.g., BERT. We employ Longformer, a BERT-like model for long documents, on the MS MARCO document re-ranking task. The complete code used for training the

    更新日期:2020-09-22
  • Can questions summarize a corpus? Using question generation for characterizing COVID-19 research
    arXiv.cs.IR Pub Date : 2020-09-19
    Gabriela Surita; Rodrigo Nogueira; Roberto Lotufo

    What are the latent questions on some textual data? In this work, we investigate using question generation models for exploring a collection of documents. Our method, dubbed corpus2question, consists of applying a pre-trained question generation model over a corpus and aggregating the resulting questions by frequency and time. This technique is an alternative to methods such as topic modelling and

    更新日期:2020-09-22
  • Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
    arXiv.cs.IR Pub Date : 2020-09-19
    Zheni Zeng; Chaojun Xiao; Yuan Yao; Ruobing Xie; Zhiyuan Liu; Fen Lin; Leyu Lin; Maosong Sun

    Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems. In this survey, we first provide a review of recommender

    更新日期:2020-09-22
  • Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context
    arXiv.cs.IR Pub Date : 2020-09-19
    Milena Filipovic; Blagoj Mitrevski; Diego Antognini; Emma Lejal Glaude; Boi Faltings; Claudiu Musat

    Simulating online recommender system performance is notoriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evaluate model performance on randomly sampled targets, yet the same systems are later used to predict user behavior sequentially from a fixed point in time. This disparity

    更新日期:2020-09-22
  • Cross-Modal Alignment with Mixture Experts Neural Network for Intral-City Retail Recommendation
    arXiv.cs.IR Pub Date : 2020-09-17
    Po Li; Lei Li; Yan Fu; Jun Rong; Yu Zhang

    In this paper, we introduce Cross-modal Alignment with mixture experts Neural Network (CameNN) recommendation model for intral-city retail industry, which aims to provide fresh foods and groceries retailing within 5 hours delivery service arising for the outbreak of Coronavirus disease (COVID-19) pandemic around the world. We propose CameNN, which is a multi-task model with three tasks including Image

    更新日期:2020-09-22
  • Towards application-specific query processing systems
    arXiv.cs.IR Pub Date : 2020-09-21
    Dimitrios VasilasDELYS, SU; Marc ShapiroDELYS, SU; Bradley KingDELYS, SU; Sara HamoudaDELYS, SU

    Database systems use query processing subsystems for enabling efficient query-based data retrieval. An essential aspect of designing any query-intensive application is tuning the query system to fit the application's requirements and workload characteristics. However, the configuration parameters provided by traditional database systems do not cover the design decisions and trade-offs that arise from

    更新日期:2020-09-22
  • div2vec: Diversity-Emphasized Node Embedding
    arXiv.cs.IR Pub Date : 2020-09-21
    Jisu Jeong; Jeong-Min Yun; Hongi Keam; Young-Jin Park; Zimin Park; Junki Cho

    Recently, the interest of graph representation learning has been rapidly increasing in recommender systems. However, most existing studies have focused on improving accuracy, but in real-world systems, the recommendation diversity should be considered as well to improve user experiences. In this paper, we propose the diversity-emphasized node embedding div2vec, which is a random walk-based unsupervised

    更新日期:2020-09-22
  • COPOD: Copula-Based Outlier Detection
    arXiv.cs.IR Pub Date : 2020-09-20
    Zheng Li; Yue Zhao; Nicola Botta; Cezar Ionescu; Xiyang Hu

    Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs

    更新日期:2020-09-22
  • UniNet: Next Term Course Recommendation using Deep Learning
    arXiv.cs.IR Pub Date : 2020-09-20
    Nicolas Araque; Germano Rojas; Maria Vitali

    Course enrollment recommendation is a relevant task that helps university students decide what is the best combination of courses to enroll in the next term. In particular, recommender system techniques like matrix factorization and collaborative filtering have been developed to try to solve this problem. As these techniques fail to represent the time-dependent nature of academic performance datasets

    更新日期:2020-09-22
  • Bid Shading by Win-Rate Estimation and Surplus Maximization
    arXiv.cs.IR Pub Date : 2020-09-19
    Shengjun Pan; Brendan Kitts; Tian Zhou; Hao He; Bharatbhushan Shetty; Aaron Flores; Djordje Gligorijevic; Junwei Pan; Tingyu Mao; San Gultekin; Jianlong Zhang

    This paper describes a new win-rate based bid shading algorithm (WR) that does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform (SSP). The method uses a modified logistic regression to predict the profit from each possible shaded bid price. The function form allows fast maximization at run-time, a key requirement for Real-Time Bidding (RTB) systems. We report production results

    更新日期:2020-09-22
  • Deliberate Self-Attention Network with Uncertainty Estimation for Multi-Aspect Review Rating Prediction
    arXiv.cs.IR Pub Date : 2020-09-18
    Tian Shi; Ping Wang; Chandan K. Reddy

    In recent years, several online platforms have seen a rapid increase in the number of review systems that request users to provide aspect-level feedback. Multi-Aspect Rating Prediction (MARP), where the goal is to predict the ratings from a review at an individual aspect level, has become a challenging and an imminent problem. To tackle this challenge, we propose a deliberate self-attention deep neural

    更新日期:2020-09-22
  • A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection
    arXiv.cs.IR Pub Date : 2020-09-18
    Tian Shi; Liuqing Li; Ping Wang; Chandan K. Reddy

    Unsupervised aspect detection (UAD) aims at automatically extracting interpretable aspects and identifying aspect-specific segments (such as sentences) from online reviews. However, recent deep learning-based topic models, specifically aspect-based autoencoder, suffer from several problems, such as extracting noisy aspects and poorly mapping aspects discovered by models to the aspects of interest.

    更新日期:2020-09-22
  • The Relationship between Deteriorating Mental Health Conditions and Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study
    arXiv.cs.IR Pub Date : 2020-09-05
    Anis Zaman; Boyu Zhang; Ehsan Hoque; Vincent Silenzio; Henry Kautz

    Mental health problems among the global population are worsened during the coronavirus disease (COVID-19). How individuals engage with online platforms such as Google Search and YouTube undergoes drastic shifts due to pandemic and subsequent lockdowns. Such ubiquitous daily behaviors on online platforms have the potential to capture and correlate with clinically alarming deteriorations in mental health

    更新日期:2020-09-22
  • COVID-19 Literature Topic-Based Search via Hierarchical NMF
    arXiv.cs.IR Pub Date : 2020-09-07
    Rachel Grotheer; Yihuan Huang; Pengyu Li; Elizaveta Rebrova; Deanna Needell; Longxiu Huang; Alona Kryshchenko; Xia Li; Kyung Ha; Oleksandr Kryshchenko

    A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics

    更新日期:2020-09-22
  • DVE: Dynamic Variational Embeddings with Applications in Recommender Systems
    arXiv.cs.IR Pub Date : 2020-08-27
    Meimei Liu; Hongxia Yang

    Embedding is a useful technique to project a high-dimensional feature into a low-dimensional space, and it has many successful applications including link prediction, node classification and natural language processing. Current approaches mainly focus on static data, which usually lead to unsatisfactory performance in applications involving large changes over time. How to dynamically characterize the

    更新日期:2020-09-21
  • Personalized TV Recommendation: Fusing User Behavior and Preferences
    arXiv.cs.IR Pub Date : 2020-08-30
    Sheng-Chieh Lin; Ting-Wei Lin; Jing-Kai Lou; Ming-Feng Tsai; Chuan-Ju Wang

    In this paper, we propose a two-stage ranking approach for recommending linear TV programs. The proposed approach first leverages user viewing patterns regarding time and TV channels to identify potential candidates for recommendation and then further leverages user preferences to rank these candidates given textual information about programs. To evaluate the method, we conduct empirical studies on

    更新日期:2020-09-21
  • Keyword Search Engine Enriched by Expert System Features
    arXiv.cs.IR Pub Date : 2020-08-28
    Olegs Verhodubs

    Keyword search engines are essential elements of large information spaces. The largest information space is the Web, and keyword search engines play crucial role there. The advent of keyword search engines has provided a quantum leap in the development of the Web. Since then, the Web has continued to evolve, and keyword search systems have proven inadequate. A new quantum leap in the development of

    更新日期:2020-09-21
  • Neural Fair Collaborative Filtering
    arXiv.cs.IR Pub Date : 2020-09-02
    Rashidul Islam; Kamrun Naher Keya; Ziqian Zeng; Shimei Pan; James Foulds

    A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical

    更新日期:2020-09-21
  • Exploration in two-stage recommender systems
    arXiv.cs.IR Pub Date : 2020-09-01
    Jiri Hron; Karl Krauth; Michael I. Jordan; Niki Kilbertus

    Two-stage recommender systems are widely adopted in industry due to their scalability and maintainability. These systems produce recommendations in two steps: (i) multiple nominators preselect a small number of items from a large pool using cheap-to-compute item embeddings; (ii) with a richer set of features, a ranker rearranges the nominated items and serves them to the user. A key challenge of this

    更新日期:2020-09-21
  • Implicit Feedback Deep Collaborative Filtering Product Recommendation System
    arXiv.cs.IR Pub Date : 2020-09-08
    Karthik Raja Kalaiselvi Bhaskar; Deepa Kundur; Yuri Lawryshyn

    In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviors. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. CF with Neural Collaborative Filtering (NCF) was shown to produce

    更新日期:2020-09-21
  • HyperFair: A Soft Approach to Integrating Fairness Criteria
    arXiv.cs.IR Pub Date : 2020-09-05
    Charles Dickens; Rishika Singh; Lise Getoor

    Recommender systems are being employed across an increasingly diverse set of domains that can potentially make a significant social and individual impact. For this reason, considering fairness is a critical step in the design and evaluation of such systems. In this paper, we introduce HyperFair, a general framework for enforcing soft fairness constraints in a hybrid recommender system. HyperFair models

    更新日期:2020-09-21
  • A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section
    arXiv.cs.IR Pub Date : 2020-09-11
    Haihua Chen

    In the era of big scholarly data, researchers frequently encounter the following problems when writing scientific articles: 1) it's challenging to select appropriate references to support the research idea, and 2) literature review is not conducted extensively, which leads to working on a research problem that has been well addressed by others. Citation recommendation assists researchers to decide

    更新日期:2020-09-21
  • Boosting Retailer Revenue by Generated Optimized Combined Multiple Digital Marketing Campaigns
    arXiv.cs.IR Pub Date : 2020-09-09
    Yafei Xu; Tian Xie; Yu Zhang

    Campaign is a frequently employed instrument in lifting up the GMV (Gross Merchandise Volume) of retailer in traditional marketing. As its counterpart in online context, digital-marketing-campaign (DMC) has being trending in recent years with the rapid development of the e-commerce. However, how to empower massive sellers on the online retailing platform the capacity of applying combined multiple digital

    更新日期:2020-09-21
  • A Knowledge Graph based Approach for Mobile Application Recommendation
    arXiv.cs.IR Pub Date : 2020-09-18
    Mingwei Zhang; Jiawei Zhao; Hai Dong; Ke Deng; Ying Liu

    With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders. How to effectively organize and make full use of rich side information of users and apps is a key challenge to address the sparsity issue for traditional approaches. To meet this challenge, we proposed

    更新日期:2020-09-21
  • NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative
    arXiv.cs.IR Pub Date : 2020-09-18
    Kumud Chauhan

    Millions of people around the world are sharing COVID-19 related information on social media platforms. Since not all the information shared on the social media is useful, a machine learning system to identify informative posts can help users in finding relevant information. In this paper, we present a BERT classifier system for W-NUT2020 Shared Task 2: Identification of Informative COVID-19 English

    更新日期:2020-09-21
  • Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting
    arXiv.cs.IR Pub Date : 2020-09-11
    Markus Viljanen; Jukka Vahlo; Aki Koponen; Tapio Pahikkala

    Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the data sets include features that describe games or players. Collaborative filtering has been found to be the most accurate predictor of past interactions. However, it can only be applied to predict new interactions for those

    更新日期:2020-09-21
  • The Capacity of Multi-user Private Information Retrieval for Computationally Limited Databases
    arXiv.cs.IR Pub Date : 2020-09-18
    William Barnhart; Zhi Tian

    We present a private information retrieval (PIR) scheme that allows a user to retrieve a single message from an arbitrary number of databases by colluding with other users while hiding the desired message index. This scheme is of particular significance when there is only one accessible database -- a special case that turns out to be more challenging for PIR in the multi-database case. The upper bound

    更新日期:2020-09-21
  • Generation-Augmented Retrieval for Open-domain Question Answering
    arXiv.cs.IR Pub Date : 2020-09-17
    Yuning Mao; Pengcheng He; Xiaodong Liu; Yelong Shen; Jianfeng Gao; Jiawei Han; Weizhu Chen

    Conventional sparse retrieval methods such as TF-IDF and BM25 are simple and efficient, but solely rely on lexical overlap and fail to conduct semantic matching. Recent dense retrieval methods learn latent representations to tackle the lexical mismatch problem, while being more computationally expensive and sometimes insufficient for exact matching as they embed the entire text sequence into a single

    更新日期:2020-09-21
  • Learning to Personalize for Web Search Sessions
    arXiv.cs.IR Pub Date : 2020-09-17
    Saad Aloteibi; Stephen Clark

    The task of session search focuses on using interaction data to improve relevance for the user's next query at the session level. In this paper, we formulate session search as a personalization task under the framework of learning to rank. Personalization approaches re-rank results to match a user model. Such user models are usually accumulated over time based on the user's browsing behaviour. We use

    更新日期:2020-09-20
  • Online Algorithms for Estimating Change Rates of Web Pages
    arXiv.cs.IR Pub Date : 2020-09-17
    Konstantin Avrachenkov; Kishor Patil; Gugan Thoppe

    For providing quick and accurate search results, a search engine maintains a local snapshot of the entire web. And, to keep this local cache fresh, it employs a crawler for tracking changes across various web pages. It would have been ideal if the crawler managed to update the local snapshot as soon as a page changed on the web. However, finite bandwidth availability and server restrictions mean that

    更新日期:2020-09-20
  • A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
    arXiv.cs.IR Pub Date : 2020-09-17
    Mariona Coll Ardanuy; Kasra Hosseini; Katherine McDonough; Amrey Krause; Daniel van Strien; Federico Nanni

    Recognizing toponyms and resolving them to their real-world referents is required for providing advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of identifying the potential entities that can be referred to by a toponym previously recognized. While it has traditionally received little attention in the

    更新日期:2020-09-20
  • Multi-modal Summarization for Video-containing Documents
    arXiv.cs.IR Pub Date : 2020-09-17
    Xiyan Fu; Jun Wang; Zhenglu Yang

    Summarization of multimedia data becomes increasingly significant as it is the basis for many real-world applications, such as question answering, Web search, and so forth. Most existing multi-modal summarization works however have used visual complementary features extracted from images rather than videos, thereby losing abundant information. Hence, we propose a novel multi-modal summarization task

    更新日期:2020-09-20
  • Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis
    arXiv.cs.IR Pub Date : 2020-09-16
    Xiaoyu Xing; Zhijing Jin; Di Jin; Bingning Wang; Qi Zhang; Xuanjing Huang

    Aspect-based sentiment analysis (ABSA) aims to predict the sentiment towards a specific aspect in the text. However, existing ABSA test sets cannot be used to probe whether a model can distinguish the sentiment of the target aspect from the non-target aspects. To solve this problem, we develop a simple but effective approach to enrich ABSA test sets. Specifically, we generate new examples to disentangle

    更新日期:2020-09-20
  • Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text
    arXiv.cs.IR Pub Date : 2020-09-16
    Yadan Fan; Sicheng Zhou; Yifan Li; Rui Zhang

    The objective of our work is to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DS) in clinical text. Two tasks were performed in this study. For the named entity recognition (NER) task, Bi-LSTM-CRF (Bidirectional Long-Short-Term-Memory Conditional Random Fields) and BERT (Bidirectional Encoder Representations from Transformers)

    更新日期:2020-09-18
  • Simplified TinyBERT: Knowledge Distillation for Document Retrieval
    arXiv.cs.IR Pub Date : 2020-09-16
    Xuanang Chen; Ben He; Kai Hui; Le Sun; Yingfei Sun

    Despite the effectiveness of utilizing BERT for document ranking, the computational cost of such approaches is non-negligible when compared to other retrieval methods. To this end, this paper first empirically investigates the applications of knowledge distillation models on document ranking task. In addition, on top of the recent TinyBERT, two simplifications are proposed. Evaluation on MS MARCO document

    更新日期:2020-09-18
  • CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
    arXiv.cs.IR Pub Date : 2020-09-16
    Tara Safavi; Danai Koutra

    We present CoDEx, a set of knowledge graph Completion Datasets Extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of entities and relations, and tens of thousands of hard negative triples that are plausible

    更新日期:2020-09-18
  • Characterizing Attitudinal Network Graphs through Frustration Cloud
    arXiv.cs.IR Pub Date : 2020-09-16
    Lucas Rusnak; Jelena Tešić

    Attitudinal Network Graphs (ANG) are network graphs where edges capture an expressed opinion: two vertices connected by an edge can be agreeable (positive) or antagonistic (negative). Measure of consensus in attitudinal graph reflects how easy or difficult consensus can be reached that is acceptable by everyone. Frustration index is one such measure as it determines the distance of a network from a

    更新日期:2020-09-18
  • Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation
    arXiv.cs.IR Pub Date : 2020-09-16
    Elena V. Epure; Guillaume Salha; Romain Hennequin

    Annotating music items with music genres is crucial for music recommendation and information retrieval, yet challenging given that music genres are subjective concepts. Recently, in order to explicitly consider this subjectivity, the annotation of music items was modeled as a translation task: predict for a music item its music genres within a target vocabulary or taxonomy (tag system) from a set of

    更新日期:2020-09-18
  • Reinforcement Learning for Strategic Recommendations
    arXiv.cs.IR Pub Date : 2020-09-15
    Georgios Theocharous; Yash Chandak; Philip S. Thomas; Frits de Nijs

    Strategic recommendations (SR) refer to the problem where an intelligent agent observes the sequential behaviors and activities of users and decides when and how to interact with them to optimize some long-term objectives, both for the user and the business. These systems are in their infancy in the industry and in need of practical solutions to some fundamental research challenges. At Adobe research

    更新日期:2020-09-18
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