Latest in

total 5393took 0.11s
Model Explanations under CalibrationJun 18 2019Explaining and interpreting the decisions of recommender systems are becoming extremely relevant both, for improving predictive performance, and providing valid explanations to users. While most of the recent interest has focused on providing local explanations, ... More
Query Generation for Patent Retrieval with Keyword Extraction based on Syntactic FeaturesJun 18 2019This paper describes a new method to extract relevant keywords from patent claims, as part of the task of retrieving other patents with similar claims (search for prior art). The method combines a qualitative analysis of the writing style of the claims ... More
Modeling Music Modality with a Key-Class Invariant Pitch Chroma CNNJun 17 2019This paper presents a convolutional neural network (CNN) that uses input from a polyphonic pitch estimation system to predict perceived minor/major modality in music audio. The pitch activation input is structured to allow the first CNN layer to compute ... More
Practical User Feedback-driven Internal Search Using Online Learning to RankJun 15 2019We present a system, Spoke, for creating and searching internal knowledge base (KB) articles for organizations. Spoke is available as a SaaS (Software-as-a-Service) product deployed across hundreds of organizations with a diverse set of domains. Spoke ... More
Relevance Feedback with Latent Variables in Riemann spacesJun 15 2019In this paper we develop and evaluate two methods for relevance feedback based on endowing a suitable "semantic query space" with a Riemann metric derived from the probability distribution of the positive samples of the feedback. The first method uses ... More
A/B Testing Measurement Framework for Recommendation Models Based on Expected RevenueJun 14 2019We provide a method to determine whether a new recommendation system improves the revenue per visit (RPV) compared to the status quo. We achieve our goal by splitting RPV into conversion rate and average order value (AOV). We use the two-part test suggested ... More
Dynamic Path-Decomposed TriesJun 14 2019A keyword dictionary is an associative array whose keys are strings. Recent applications handling massive keyword dictionaries in main memory have a need for a space-efficient implementation. When limited to static applications, there are a number of ... More
Scalable Knowledge Graph Construction from TwitterJun 14 2019We describe a knowledge graph derived from Twitter data with the goal of discovering relationships between people, links, and topics. The goal is to filter out noise from Twitter and surface an inside-out view that relies on high quality content. The ... More
$c^+$GAN: Complementary Fashion Item RecommendationJun 13 2019We present a conditional generative adversarial model to draw realistic samples from paired fashion clothing distribution and provide real samples to pair with arbitrary fashion units. More concretely, given an image of a shirt, obtained from a fashion ... More
Topic Modeling via Full Dependence MixturesJun 13 2019We consider the topic modeling problem for large datasets. For this problem, Latent Dirichlet Allocation (LDA) with a collapsed Gibbs sampler optimization is the state-of-the-art approach in terms of topic quality. However, LDA is a slow approach, and ... More
A Comparison of Word-based and Context-based Representations for Classification Problems in Health InformaticsJun 13 2019Distributed representations of text can be used as features when training a statistical classifier. These representations may be created as a composition of word vectors or as context-based sentence vectors. We compare the two kinds of representations ... More
Figurative Usage Detection of Symptom Words to Improve Personal Health Mention DetectionJun 13 2019Personal health mention detection deals with predicting whether or not a given sentence is a report of a health condition. Past work mentions errors in this prediction when symptom words, i.e. names of symptoms of interest, are used in a figurative sense. ... More
FPScreen: A Rapid Similarity Search Tool for Massive Molecular Library Based on Molecular Fingerprint ComparisonJun 13 2019We designed a fast similarity search engine for large molecular libraries: FPScreen. We downloaded 100 million molecules' structure files in PubChem with SDF extension, then applied a computational chemistry tool RDKit to convert each structure file into ... More
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical ApplicationsJun 12 2019We present a simple text mining method that is easy to implement, requires minimal data collection and preparation, and is easy to use for proposing ranked associations between a list of target terms and a key phrase. We call this method KinderMiner, ... More
Reinforcement Knowledge Graph Reasoning for Explainable RecommendationJun 12 2019Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate ... More
A decentralized trust-aware collaborative filtering recommender system based on weighted items for social tagging systemsJun 12 2019Recommender systems are used with the purpose of suggesting contents and resources to the users in a social network. These systems use ranks or tags each user assign to different resources to predict or make suggestions to users. Lately, social tagging ... More
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order MotifsJun 12 2019In this paper, we introduce the notion of motif closure and describe higher-order ranking and link prediction methods based on the notion of closing higher-order network motifs. The methods are fast and efficient for real-time ranking and link prediction-based ... More
Real-time Attention Based Look-alike Model for Recommender SystemJun 12 2019Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Matthew effect" becomes increasingly evident. While the head contents get ... More
BiSET: Bi-directional Selective Encoding with Template for Abstractive SummarizationJun 12 2019The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by ... More
Joint Reasoning for Temporal and Causal RelationsJun 12 2019Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and causal relations are closely related and one relation even dictates the other ... More
CogCompTime: A Tool for Understanding Time in Natural Language TextJun 12 2019Automatic extraction of temporal information in text is an important component of natural language understanding. It involves two basic tasks: (1) Understanding time expressions that are mentioned explicitly in text (e.g., February 27, 1998 or tomorrow), ... More
From Fully Supervised to Zero Shot Settings for Twitter Hashtag RecommendationJun 11 2019We propose a comprehensive end-to-end pipeline for Twitter hashtags recommendation system including data collection, supervised training setting and zero shot training setting. In the supervised training setting, we have proposed and compared the performance ... More
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural NetworkJun 11 2019Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies. Existing methods do not fully exploit such dependencies. We present a novel inter-sentence ... More
Innovating HR Using an Expert System for Recruiting IT Specialists -- ESRITJun 11 2019One of the most rapidly evolving and dynamic business sector is the IT domain, where there is a problem finding experienced, skilled and qualified employees. Specialists are essential for developing and implementing new ideas into products. Human resources ... More
The snippets taxonomy in web search enginesJun 11 2019In this paper authors analyzed 50 000 keywords results collected from localized Polish Google search engine. We proposed a taxonomy for snippets displayed in search results as regular, rich, news, featured and entity types snippets. We observed some correlations ... More
EXmatcher: Combining Features Based on Reference Strings and Segments to Enhance Citation MatchingJun 11 2019Citation matching is a challenging task due to different problems such as the variety of citation styles, mistakes in reference strings and the quality of identified reference segments. The classic citation matching configuration used in this paper is ... More
Modeling the Past and Future Contexts for Session-based RecommendationJun 11 2019Jun 12 2019Long session-based recommender systems have attacted much attention recently. For each user, they may create hundreds of click behaviors in short time. To learn long session item dependencies, previous sequential recommendation models resort either to ... More
Modeling the Past and Future Contexts for Session-based RecommendationJun 11 2019Long session-based recommender systems have attacted much attention recently. For each user, they may create hundreds of click behaviors in short time. To learn long session item dependencies, previous sequential recommendation models resort either to ... More
Coupled Variational Recurrent Collaborative FilteringJun 11 2019We focus on the problem of streaming recommender system and explore novel collaborative filtering algorithms to handle the data dynamicity and complexity in a streaming manner. Although deep neural networks have demonstrated the effectiveness of recommendation ... More
Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive CodingJun 11 2019Active learning is a popular methodology in text classification - known in the legal domain as "predictive coding" or "Technology Assisted Review" or "TAR" - due to its potential to minimize the required review effort to build effective classifiers. In ... More
Representation Learning-Assisted Click-Through Rate PredictionJun 11 2019Click-through rate (CTR) prediction is a critical task in online advertising systems. Most existing methods mainly model the feature-CTR relationship and suffer from the data sparsity issue. In this paper, we propose DeepMCP, which models other types ... More
Towards Amortized Ranking-Critical Training for Collaborative FilteringJun 10 2019Collaborative filtering is widely used in modern recommender systems. Recent research shows that variational autoencoders (VAEs) yield state-of-the-art performance by integrating flexible representations from deep neural networks into latent variable ... More
Deep Learning-Based Automatic Downbeat Tracking: A Brief ReviewJun 10 2019As an important format of multimedia, music has filled almost everyone's life. Automatic analyzing music is a significant step to satisfy people's need for music retrieval and music recommendation in an effortless way. Thereinto, downbeat tracking has ... More
Deep Spatio-Temporal Neural Networks for Click-Through Rate PredictionJun 10 2019Click-through rate (CTR) prediction is a critical task in online advertising systems. A large body of research considers each ad independently, but ignores its relationship to other ads that may impact the CTR. In this paper, we investigate various types ... More
A cost-reducing partial labeling estimator in text classification problemJun 10 2019We propose a new approach to address the text classification problems when learning with partial labels is beneficial. Instead of offering each training sample a set of candidate labels, we assign negative-oriented labels to the ambiguous training examples ... More
Variance Reduction in Gradient Exploration for Online Learning to RankJun 10 2019Online Learning to Rank (OL2R) algorithms learn from implicit user feedback on the fly. The key of such algorithms is an unbiased estimation of gradients, which is often (trivially) achieved by uniformly sampling from the entire parameter space. This ... More
Variance Reduction in Gradient Exploration for Online Learning to RankJun 10 2019Jun 14 2019Online Learning to Rank (OL2R) algorithms learn from implicit user feedback on the fly. The key of such algorithms is an unbiased estimation of gradients, which is often (trivially) achieved by uniformly sampling from the entire parameter space. This ... More
Deep Music Analogy Via Latent Representation DisentanglementJun 09 2019Analogy is a key solution to automated music generation, featured by its ability to generate both natural and creative pieces based on only a few examples. In general, an analogy is made by partially transferring the music abstractions, i.e., high-level ... More
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text ClassificationJun 09 2019CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. However, most existing deep models for multi-label text classification ... More
Recovering Variable Names for Minified Code with Usage ContextsJun 08 2019In modern Web technology, JavaScript (JS) code plays an important role. To avoid the exposure of original source code, the variable names in JS code deployed in the wild are often replaced by short, meaningless names, thus making the code extremely difficult ... More
Adversarial Mahalanobis Distance-based Attentive Song Recommender for Automatic Playlist ContinuationJun 08 2019In this paper, we aim to solve the automatic playlist continuation (APC) problem by modeling complex interactions among users, playlists, and songs using only their interaction data. Prior methods mainly rely on dot product to account for similarities, ... More
Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook CommentsJun 08 2019The most of the people have their account on social networks (e.g. Facebook, Vkontakte) where they express their attitude to different situations and events. Facebook provides only the positive mark as a like button and share. However, it is important ... More
Collaborating with Users in Proximity for Decentralized Mobile Recommender SystemsJun 07 2019Typically, recommender systems from any domain, be it movies, music, restaurants, etc., are organized in a centralized fashion. The service provider holds all the data, biases in the recommender algorithms are not transparent to the user, and the service ... More
A Tree Pattern Matching Algorithm for XML Queries with Structural PreferencesJun 07 2019In the XML community, exact queries allow users to specify exactly what they want to check and/or retrieve in an XML document. When they are applied to a semi-structured document or to a document with an overly complex model, the lack or the ignorance ... More
Learning to Recommend Third-Party Library Migration Opportunities at the API LevelJun 07 2019The manual migration between different third-party libraries represents a challenge for software developers. Developers typically need to explore both libraries Application Programming Interfaces, along with reading their documentation, in order to locate ... More
Quaternion Collaborative Filtering for RecommendationJun 06 2019This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Quaternion algebra, benefiting from the expressiveness and rich representation ... More
Cross-Modal Interaction Networks for Query-Based Moment Retrieval in VideosJun 06 2019Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one aspect of this emerging task, such as the query representation learning, ... More
Sparse Parallel Training of Hierarchical Dirichlet Process Topic ModelsJun 06 2019Nonparametric extensions of topic models such as Latent Dirichlet Allocation, including Hierarchical Dirichlet Process (HDP), are often studied in natural language processing. Training these models generally requires use of serial algorithms, which limits ... More
Comprehensive Personalized Ranking Using One-Bit Comparison DataJun 06 2019The task of a personalization system is to recommend items or a set of items according to the users' taste, and thus predicting their future needs. In this paper, we address such personalized recommendation problems for which one-bit comparison data of ... More
Context Attentive Document Ranking and Query SuggestionJun 05 2019We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural network is introduced to learn search context representation of individual ... More
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech DetectionJun 05 2019Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the ... More
A Passage-Based Approach to Learning to Rank DocumentsJun 05 2019According to common relevance-judgments regimes, such as TREC's, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: ... More
Enhancing interoperable datasets with virtual linksJun 05 2019To achieve semantic interoperability, numerous data standards, ontologies, and controlled vocabularies have been developed and adopted by the industry and scientific communities. Yet, semantic heterogeneity remains a problem when interoperating data from ... More
Evaluation and Improvement of Chatbot Text Classification Data Quality Using Plausible Negative ExamplesJun 05 2019We describe and validate a metric for estimating multi-class classifier performance based on cross-validation and adapted for improvement of small, unbalanced natural-language datasets used in chatbot design. Our experiences draw upon building recruitment ... More
Fair Near Neighbor Search: Independent Range Sampling in High DimensionsJun 05 2019Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. There are several variants of the similarity search problem, and one of the most relevant is the $r$-near neighbor ($r$-NN) problem: given a radius ... More
Binarized Collaborative Filtering with Distilling Graph Convolutional NetworksJun 05 2019The efficiency of top-K item recommendation based on implicit feedback are vital to recommender systems in real world, but it is very challenging due to the lack of negative samples and the large number of candidate items. To address the challenges, we ... More
Motivo: fast motif counting via succinct color coding and adaptive samplingJun 04 2019The randomized technique of color coding is behind state-of-the-art algorithms for estimating graph motif counts. Those algorithms, however, are not yet capable of scaling well to very large graphs with billions of edges. In this paper we develop novel ... More
Collaborative Translational Metric LearningJun 04 2019Recently, matrix factorization-based recommendation methods have been criticized for the problem raised by the triangle inequality violation. Although several metric learning-based approaches have been proposed to overcome this issue, existing approaches ... More
Toward Building Conversational Recommender Systems: A Contextual Bandit ApproachJun 04 2019Contextual bandit algorithms have gained increasing popularity in recommender systems, because they can learn to adapt recommendations by making exploration-exploitation trade-off. Recommender systems equipped with traditional contextual bandit algorithms ... More
The FacT: Taming Latent Factor Models for Explainability with Factorization TreesJun 03 2019Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use ... More
Contextually Propagated Term Weights for Document RepresentationJun 03 2019Word embeddings predict a word from its neighbours by learning small, dense embedding vectors. In practice, this prediction corresponds to a semantic score given to the predicted word (or term weight). We present a novel model that, given a target word, ... More
Unsupervised Neural Generative Semantic HashingJun 03 2019Fast similarity search is a key component in large-scale information retrieval, where semantic hashing has become a popular strategy for representing documents as binary hash codes. Recent advances in this area have been obtained through neural network ... More
Federated Hierarchical Hybrid Networks for Clickbait DetectionJun 03 2019Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization. As the adverse effects of clickbait attract more and more attention, researchers ... More
Evaluating Non-aligned Musical Score Transcriptions with MV2HJun 03 2019The original MV2H metric was designed to evaluate systems which transcribe from an input audio (or MIDI) piece to a complete musical score. However, it requires both the transcribed score and the ground truth score to be time-aligned with the input. Some ... More
Mining Data from the Congressional RecordJun 03 2019We propose a data storage and analysis method for using the US Congressional record as a policy analysis tool. We use Amazon Web Services and the Solr search engine to store and process Congressional record data from 1789 to the present, and then query ... More
Technology Knowledge Graph Based on Patent DataJun 02 2019Jun 04 2019The growing developments in general semantic networks (or knowledge graphs) have motivated us to build a large-scale comprehensive knowledge graph of engineering data for engineering knowledge discovery, technology search and retrieval, and artificial ... More
Sequential Scenario-Specific Meta Learner for Online RecommendationJun 02 2019Cold-start problems are long-standing challenges for practical recommendations. Most existing recommendation algorithms rely on extensive observed data and are brittle to recommendation scenarios with few interactions. This paper addresses such problems ... More
Question Answering as an Automatic Evaluation Metric for News Article SummarizationJun 02 2019Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, Answering Performance for Evaluation of Summaries. ... More
Sparse Bayesian Learning Approach for Discrete Signal ReconstructionJun 01 2019This study addresses the problem of discrete signal reconstruction from the perspective of sparse Bayesian learning (SBL). Generally, it is intractable to perform the Bayesian inference with the ideal discretization prior under the SBL framework. To overcome ... More
Promotion of Answer Value Measurement with Domain Effects in Community Question Answering SystemsJun 01 2019In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solutions mainly exploit the syntactic or semantic correlation between a question ... More
Emotional Embeddings: Refining Word Embeddings to Capture Emotional Content of WordsMay 31 2019Word embeddings are one of the most useful tools in any modern natural language processing expert's toolkit. They contain various types of information about each word which makes them the best way to represent the terms in any NLP task. But there are ... More
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble LearningMay 31 2019Recent advances in deep learning have facilitated the demand of neural models for real applications. In practice, these applications often need to be deployed with limited resources while keeping high accuracy. This paper touches the core of neural models ... More
Deep Learning Recommendation Model for Personalization and Recommendation SystemsMay 31 2019With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their need ... More
Evaluating Memento Service OptimizationsMay 31 2019Services and applications based on the Memento Aggregator can suffer from slow response times due to the federated search across web archives performed by the Memento infrastructure. In an effort to decrease the response times, we established a cache ... More
Leveraging Trust and Distrust in Recommender Systems via Deep LearningMay 31 2019The data scarcity of user preferences and the cold-start problem often appear in real-world applications and limit the recommendation accuracy of collaborative filtering strategies. Leveraging the selections of social friends and foes can efficiently ... More
Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam QuestionsMay 30 2019Getting an overview over the legal domain has become challenging, especially in a broad, international context. Legal question answering systems have the potential to alleviate this task by automatically retrieving relevant legal texts for a specific ... More
Multitask Text-to-Visual Embedding with Titles and Clickthrough DataMay 30 2019Text-visual (or called semantic-visual) embedding is a central problem in vision-language research. It typically involves mapping of an image and a text description to a common feature space through a CNN image encoder and a RNN language encoder. In this ... More
Deep Adversarial Social RecommendationMay 30 2019Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life. The majority of existing social recommendation methods ... More
On the Effectiveness of Low-rank Approximations for Collaborative Filtering compared to Neural NetworksMay 30 2019Even in times of deep learning, low-rank approximations by factorizing a matrix into user and item latent factors continue to be a method of choice for collaborative filtering tasks due to their great performance. While deep learning based approaches ... More
Explainable Fashion Recommendation: A Semantic Attribute Region Guided ApproachMay 30 2019In fashion recommender systems, each product usually consists of multiple semantic attributes (e.g., sleeves, collar, etc). When making cloth decisions, people usually show preferences for different semantic attributes (e.g., the clothes with v-neck collar). ... More
A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio FilesMay 30 2019The development of models for learning music similarity and feature extraction from audio media files is an increasingly important task for the entertainment industry. This work proposes a novel music classification model based on metric learning and ... More
Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric LearningMay 29 2019The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question. Such a system can be used to understand and answer rare and noisy reformulations of common questions ... More
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical MethodologyMay 29 2019May 31 2019Most practical recommender systems focus on estimating immediate user engagement without considering the long-term effects of recommendations on user behavior. Reinforcement learning (RL) methods offer the potential to optimize recommendations for long-term ... More
Predicting next shopping stage using Google Analytics data for E-commerce applicationsMay 29 2019E-commerce web applications are almost ubiquitous in our day to day life, however as useful as they are, most of them have little to no adaptation to user needs, which in turn can cause both lower conversion rates as well as unsatisfied customers. We ... More
Neural Review Rating Prediction with Hierarchical Attentions and Latent FactorsMay 29 2019Text reviews can provide rich useful semantic information for modeling users and items, which can benefit rating prediction in recommendation. Different words and reviews may have different informativeness for users or items. Besides, different users ... More
NRPA: Neural Recommendation with Personalized AttentionMay 29 2019Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. ... More
Deep Cross Networks with Aesthetic Preference for Cross-domain RecommendationMay 29 2019When purchasing appearance-first products, e.g., clothes, product appearance aesthetics plays an important role in the decision process. Moreover, user's aesthetic preference, which can be regarded as a personality trait and a basic requirement, is domain ... More
Using Micro-collections in Social Media to Generate Seeds for Web Archive CollectionsMay 29 2019In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events ranging from elections to disease outbreaks. These archived collections start with seed URIs ... More
Texture Selection for Automatic Music Genre ClassificationMay 28 2019Music Genre Classification is the problem of associating genre-related labels to digitized music tracks. It has applications in the organization of commercial and personal music collections. Often, music tracks are described as a set of timbre-inspired ... More
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer SatisfactionMay 28 2019Jun 16 2019An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. In this paper, we propose a model called Social Explorative ... More
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer SatisfactionMay 28 2019May 29 2019An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. In this paper, we propose a model called Social Explorative ... More
Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet EmbeddingMay 28 2019Recently, the applications of person re-identification in visual surveillance and human-computer interaction are sharply increasing, which signifies the critical role of such a problem. In this paper, we propose a two-stream convolutional network (ConvNet) ... More
Job Recommendation through Progression of Job SelectionMay 28 2019Job recommendation has traditionally been treated as a filter-based match or as a recommendation based on the features of jobs and candidates as discrete entities. In this paper, we introduce a methodology where we leverage the progression of job selection ... More
Highly Scalable and Flexible Model for Effective Aggregation of Context-based Data in Generic IIoT ScenariosMay 28 2019Interconnectivity of production machines is a key feature of the Industrial Internet of Things (IIoT). This feature allows for many advantages in producing. Configuration and maintenance gets easier, as access to the given production unit is not necessarily ... More
A Framework for App Store OptimizationMay 28 2019In this paper a framework for app store optimization is proposed. The framework is based on two main areas: developer dependent elements and user dependent elements. Developer dependent elements are similar factors in search engine optimization. User ... More
LambdaOpt: Learn to Regularize Recommender Models in Finer LevelsMay 28 2019Recommendation models mainly deal with categorical variables, such as user/item ID and attributes. Besides the high-cardinality issue, the interactions among such categorical variables are usually long-tailed, with the head made up of highly frequent ... More
Minimizing Time-to-Rank: A Learning and Recommendation ApproachMay 27 2019Consider the following problem faced by an online voting platform: A user is provided with a list of alternatives, and is asked to rank them in order of preference using only drag-and-drop operations. The platform's goal is to recommend an initial ranking ... More
On a scalable problem transformation method for multi-label learningMay 27 2019Binary relevance is a simple approach to solve multi-label learning problems where an independent binary classifier is built per each label. A common challenge with this in real-world applications is that the label space can be very large, making it difficult ... More
FairSearch: A Tool For Fairness in Ranked Search ResultsMay 27 2019Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine career ... More
Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender SystemsMay 27 2019Many recommendation algorithms are available to digital library recommender system operators. The effectiveness of algorithms is largely unreported by way of online evaluation. We compare a standard term-based recommendation approach to two promising ... More