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Enhancing Employer Brand Evaluation with Collaborative Topic Regression Models
Employer Brand Evaluation (EBE) is to understand an employer’s unique characteristics to identify competitive edges. Traditional approaches rely heavily on employers’ financial information, including financial reports and filings submitted to the ...
Large-Alphabet Semi-Static Entropy Coding Via Asymmetric Numeral Systems
An entropy coder takes as input a sequence of symbol identifiers over some specified alphabet and represents that sequence as a bitstring using as few bits as possible, typically assuming that the elements of the sequence are independent of each other. ...
CRSAL: Conversational Recommender Systems with Adversarial Learning
Recommender systems have been attracting much attention from both academia and industry because of their ability to capture user interests and generate personalized item recommendations. As the life pace in contemporary society speeds up, traditional ...
A Survey on Heterogeneous One-class Collaborative Filtering
Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users’ feedback toward items often contain the most significant information reflecting their preferences, which ...
Pairwise Link Prediction Model for Out of Vocabulary Knowledge Base Entities
Real-world knowledge bases such as DBPedia, Yago, and Freebase contain sparse linkage connectivity, which poses a severe challenge to link prediction between entities. To cope with such data scarcity issues, recent models have focused on learning ...
Fine-Grained Privacy Detection with Graph-Regularized Hierarchical Attentive Representation Learning
Due to the complex and dynamic environment of social media, user generated contents (UGCs) may inadvertently leak users’ personal aspects, such as the personal attributes, relationships and even the health condition, and thus place users at high privacy ...
Learning Unsupervised Knowledge-Enhanced Representations to Reduce the Semantic Gap in Information Retrieval
The semantic mismatch between query and document terms—i.e., the semantic gap—is a long-standing problem in Information Retrieval (IR). Two main linguistic features related to the semantic gap that can be exploited to improve retrieval are synonymy and ...
Explaining Text Matching on Neural Natural Language Inference
Natural language inference (NLI) is the task of detecting the existence of entailment or contradiction in a given sentence pair. Although NLI techniques could help numerous information retrieval tasks, most solutions for NLI are neural approaches whose ...
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Online ranker evaluation is one of the key challenges in information retrieval. Although the preferences of rankers can be inferred by interleaving methods, the problem of how to effectively choose the ranker pair that generates the interleaved list ...
When to Stop Reviewing in Technology-Assisted Reviews: Sampling from an Adaptive Distribution to Estimate Residual Relevant Documents
Technology-Assisted Reviews (TAR) aim to expedite document reviewing (e.g., medical articles or legal documents) by iteratively incorporating machine learning algorithms and human feedback on document relevance. Continuous Active Learning (CAL) ...
Block-Aware Item Similarity Models for Top-N Recommendation
Top-N recommendations have been studied extensively. Promising results have been achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in the estimation of item similarities. Observing the block-diagonal structure of ...
