Purpose
With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions.
Design/methodology/approach
The authors collected a large-scale Kindle Books data set containing book descriptions and ratings, and calculated whether a specific book will receive a high rating. For this purpose, several feature representation methods (bag-of-words, term frequency–inverse document frequency [TF-IDF] and Word2vec) and machine learning classifiers (logistic regression, random forest, naive Bayes and support vector machine) were used.
Findings
The used classifiers show substantial accuracy in predicting reader satisfaction. Among them, the random forest classifier combined with the TF-IDF feature representation method exhibited the highest accuracy at 96.09%.
Originality/value
This study revealed that user satisfaction can be predicted based on book descriptions and shed light on the limitations of existing recommendation systems. Further, both practical and theoretical implications have been discussed.
Jujeop is a way for K-pop fans to express their love for the K-pop stars they adore by creating a type of Korean pun through unique comments in Youtube videos that feature those Kpop stars. One of the unique characteristics of Jujeop is its use of exaggerated expressions to compliment K-pop stars, which contain or lead to humor. Based on this characteristic, Jujeop can be separated into four distinct types, with their own lexical collocations: (1) Fragmenting words to create a twist, (2) Homophones and homographs, (3) Repetition, and (4) Nonsense. Thus, the current study defines the concept of Jujeop and manually annotates the 8.6K comments into one of the four Jujeop types. With the given annotated corpus, this study presents distinctive characteristics of Jujeop comments compared to the other comments by classification task. Moreover, with the clustering approach, we proposed a structural dependency within each Jujeop type. We have made our dataset publicly available for future research of Jujeop expressions.
<abstract><p>Since information and communication technology (ICT) has become one of the leading and essential fields for allowing developing countries to have the major growth engines, the majority of the countries have promoted collaboration in every ICT-related topics. In this study, we performed the trend and collaboration network analysis (CNA) in Korea for 2010–2019 among researchers who are related to human–computer interaction, one of the hottest research areas in ICT. Publication data were collected from SciVal, and the collaboration network was determined using <italic>degree, closeness, betweenness centralities,</italic> and <italic>PageRank</italic>. Hence, key researchers were identified based on their centrality metrics. The dataset contained 7,155 publications, thus reflecting the contributions of a total of 243 authors. The results of our data analysis demonstrated that key researchers can be identified via CNA; this aspect was not evident from the results of the most productive researchers. Additionally, on the basis of the results, the implications and limitations of this study were analyzed.</p></abstract>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.