What makes your opinion popular?

Vasconcelos, Marisa; Almeida, Jussara M.; Gonçalves, Marcos André

doi:10.1145/2554850.2554911

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…By accurately predicting which type of businesses attract more visits (i.e., more check‐ins), we can provide insights for daily operation decisions (e.g., staff scheduling) and the effectiveness of promotion strategies launched on LBS. In addition, when predicting the popularity of reviews, previous studies have shown that complex machine learning models do not always outperform linear regression models (Vasconcelos et al., 2014). Thus, it is unclear which type of predictive model yields the best prediction accuracy, and we aim to address this issue by comparing linear regression, machine learning models, and deep learning models.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting venue popularity on location‐based services using interpretable machine learning

Wang

Gopal

Shankar

et al. 2022

Production and Operations Management

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Customers are increasingly utilizing location-based services via mobile devices to engage with retail establishments. The focus of this paper is to identify factors that help to drive venue popularity revealed by location-based services, which then better facilitate companies' operational decisions, such as procurement and staff scheduling. Using data collected from Foursquare and Yelp, we build, evaluate, and compare a wide variety of machine learning methods including deep learning models with varying characteristics and degrees of sophistication. First, we find that support vector regression is the best performing model compared to other complex predictive algorithms. Second, we apply SHAP (Shapley Additive exPlanations) to quantify the contribution from each business feature at both the global and local levels. The global interpretability results show that customer loyalty, the agglomeration effect, and the word-of-mouth effect are the top three drivers of venue popularity. Furthermore, the local interpretability analysis reveals that the contributions of business features vary, both quantitatively and directionally. Our findings are robust with respect to different popularity measures, training and testing periods, and prediction horizons. These findings extend our knowledge of location-based services by demonstrating their potential to play a prominent role in attracting consumer engagement and boosting venue popularity. Managers can make better operational decisions such as procurement and staff scheduling based on these more accurate venue popularity prediction methods. Furthermore, this study also highlights the importance of model interpretability which enhances the ability of managers to more effectively utilize machine learning models for effective decision-making.

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Section: Literature Reviewmentioning

confidence: 99%

Forecasting venue popularity on location‐based services using interpretable machine learning

Wang

Gopal

Shankar

et al. 2022

Production and Operations Management

View full text Add to dashboard Cite

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On the feasibility of predicting popular news at cold start

Arapakis

Cambazoğlu

Lalmas

2016

Asso for Info Science & Tech

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Prominent news sites on the web provide hundreds of news articles daily. The abundance of news content competing to attract online attention, coupled with the manual effort involved in article selection, necessitates the timely prediction of future popularity of these news articles. The future popularity of a news article can be estimated using signals indicating the article's penetration in social media (e.g., number of tweets) in addition to traditional web analytics (e.g., number of page views). In practice, it is important to make such estimations as early as possible, preferably before the article is made available on the news site (i.e., at cold start). In this paper we perform a study on cold‐start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News, a major news provider. We characterize the popularity of news articles through a set of online metrics and try to predict their values across time using machine learning techniques on a large collection of features obtained from various sources. Our findings indicate that predicting news popularity at cold start is a difficult task, contrary to the findings of a prior work on the same topic. Most articles' popularity may not be accurately anticipated solely on the basis of content features, without having the early‐stage popularity values.

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