Navigating the ever-expanding sea of scientific literature presents a daunting challenge for researchers seeking relevant and up-to-date information. Traditional citation recommendation systems, while well-intentioned, often fall short due to their ...

Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the ...

Although building a 360-degree comprehensive view of a customer has been a long-standing goal in marketing, this challenge has not been successfully addressed in many marketing applications because fractured customer data stored across different “silos” ...

Search is probably the most common activity that humans conduct all the time. A search target can be a concrete item (with a yes or no answer and location information), an abstract concept (such as the most important information on the Web about Xindong ...

Hospital readmission refers to the situation where a patient is re-hospitalized with the same primary diagnosis within a specific time interval after discharge. Hospital readmission causes $26 billion preventable expenses to the U.S. health systems ...

In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature ...

Online communities that curate knowledge critically depend on high-quality contributions from anonymous expert users. Understanding users’ motivation to contribute knowledge helps practitioners design such websites for optimal user contribution and user ...

Recommender Systems are nowadays successfully used by all major web sites—from e-commerce to social media—to filter content and make suggestions in a personalized way. Academic research largely focuses on the value of recommenders for consumers, e.g., ...

Consumers increasingly make informed buying decisions based on reading online reviews for products and services. Due to the large volume of available online reviews, consumers hardly have the time and patience to read them all. This article aims to ...

This article presents a novel system, AZEmo, which extracts and classifies emoticons from the ever-growing critical Chinese social media and E-commerce. An emoticon is a meta-communicative pictorial representation of facial expressions, which helps to ...

Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein. Conversely, question-answering systems ...

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be ...

This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups ...

Collaborative tagging or social bookmarking is a main component of Web 2.0 systems and has been widely recognized as one of the key technologies underpinning next-generation knowledge management platforms. In this article, we propose a subject-centered ...