What books will be your bestseller? A machine learning approach with Amazon Kindle

Lee, Seungpeel; Ji, Honggeun; Kim, Jina; Park, Eunil

doi:10.1108/el-08-2020-0234

Cited by 14 publications

(4 citation statements)

References 49 publications

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“…These modern devices involve the features of voice and AR that can provide Vibrio-tactical feedback affecting the consumer's reactions according to the environment (Hadi and Venezuela, 2020). For instance, Amazon's recommendation system intelligently allows the user to choose based on algorithms prediction (Lee et al. , 2021).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Influence of augmented reality on shopping behavior

Dogra

Kaushik

Kalia

et al. 2023

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PurposeDigital technologies emerged as innovative avenues for launching new products, advertising brands, increasing customer awareness and thus leaving a remarkable impact on the online marketplace. The present study analyzed the effects of crucial antecedents of AR interactive technology on customers' behavior toward AR-based e-commerce websites.Design/methodology/approachConvenience sampling was used to collect primary data from 357 iGen respondents aged 16–22 years; residing in New Delhi and the NCR region of India and examined using the structural equation modeling technique.FindingsResults revealed that technology anxiety and virtuality significantly influence customers' attitudes and behavioral intentions toward AR-based e-commerce websites. However, interactivity and innovativeness remain non-significant. Additionally, non-significant moderating effects were identified for the moderators, i.e. trust and need for touch. At the same time, gender has a significant moderating effect only for the association between technology anxiety and attitude toward AR-based e-commerce websites.Research limitations/implicationsThe study summarizes numerous theoretical and managerial implications for AR-based website designers and policymakers, followed by the crucial limitations and directions for future research.Originality/valueThe present research provides a significant understanding of the e-commerce industry by providing valuable insights about young iGen consumers' perceptions of AR-based e-commerce websites.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Influence of augmented reality on shopping behavior

Dogra

Kaushik

Kalia

et al. 2023

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“…In the process of splitting data, the data in the child nodes has a smaller standard deviation than the data in the parent node, resulting in the data in the child nodes being more pure. M5 selects the split that will result in the greatest predicted error reduction after taking into account all feasible splits [16]. In many cases, this division leads to the formation of a gigantic tree-like structure, which is characterized by overfitting.…”

Section: Model Tree M5pmentioning

confidence: 99%

Soil Infiltration Rate Impact on Water Quality Modeled Using Random Forest Regression

Sopandi

2021

J. Appl. Data Sci.

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In this paper, Infiltration rate of the soil is investigated by using predictive models of Random forest regression and their performance were compared with Artificial neural network (ANN) and M5P model tree techniques. We utilized 132 field measurements comprising this dataset. 88 models were trained using observations, while the remaining 44 were used to validate it. The cumulative time (Tf), the impurity type (It), the impurity concentration (Ci), and the moisture content (Wc) were utilized as input variables, and the rate of infiltration was employed as the output. To evaluate the efficiency of the two modeling methodologies, correlation coefficients we estimated root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative square error are all terms that may be used to describe errors (RRSE). The random forest regression approach outperforms the other two models when compared to evolutionary data (ANN and M5P model tree). Using a random forest as a model, regression can properly estimate the infiltration rate within a 25% error range. According to the results of the sensitivity research, cumulative time plays an important influence in determining the soil's penetration rate.

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“…Among the several aspects that are more likely to influence the visibility and accomplishment of an artistic piece, we have its intrinsic quality, innovation, and affinity with the main trends, interests, and expectations predominating in a given period and place. All these three main aspects are not only challenging to define but even more so to predict, which has motivated growing interest from the scientific community (e.g., [6][7][8][9][10][11]).…”

Section: Introductionmentioning

confidence: 99%

Using full-text content to characterize and identify best seller books: A study of early 20th-century literature

da Silva,

Silva,

de Arruda

et al. 2024

PLoS ONE

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Artistic pieces can be studied from several perspectives, one example being their reception among readers over time. In the present work, we approach this interesting topic from the standpoint of literary works, particularly assessing the task of predicting whether a book will become a best seller. Unlike previous approaches, we focused on the full content of books and considered visualization and classification tasks. We employed visualization for the preliminary exploration of the data structure and properties, involving SemAxis and linear discriminant analyses. To obtain quantitative and more objective results, we employed various classifiers. Such approaches were used along with a dataset containing (i) books published from 1895 to 1923 and consecrated as best sellers by the Publishers Weekly Bestseller Lists and (ii) literary works published in the same period but not being mentioned in that list. Our comparison of methods revealed that the best-achieved result—combining a bag-of-words representation with a logistic regression classifier—led to an average accuracy of 0.75 both for the leave-one-out and 10-fold cross-validations. Such an outcome enhances the difficulty in predicting the success of books with high accuracy, even using the full content of the texts. Nevertheless, our findings provide insights into the factors leading to the relative success of a literary work.

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What books will be your bestseller? A machine learning approach with Amazon Kindle

Cited by 14 publications

References 49 publications

Influence of augmented reality on shopping behavior

Influence of augmented reality on shopping behavior

Soil Infiltration Rate Impact on Water Quality Modeled Using Random Forest Regression

Using full-text content to characterize and identify best seller books: A study of early 20th-century literature

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