Of Wines and Reviews: Measuring and Modeling the Vivino Wine Social Network

Kotonya, Neema; Cristofaro, Paolo De; Cristofaro, Emiliano De

doi:10.1109/asonam.2018.8508776

Cited by 9 publications

(11 citation statements)

References 12 publications

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“…Kotonya et al . () analysed the content and construction Vivino ratings and found that community comments and ratings express similar rich knowledge of wines as experts, wines are typically assessed within geographical regions and the analysis of website usage points to the absence of spam and/or trolls affecting the credibility of the ratings. This evidence points to some credibility for the community ratings data employed.…”

Section: Data and Empirical Specificationmentioning

confidence: 99%

“…Vivino (https://www.vivino.com/wine-news/vivino-5-star-rating-system) claim credibility of their community ratings pointing to strong correlations between their community ratings and a number of wine experts and also suggest they are not influenced by advertising or sponsorship. Kotonya et al (2018) analysed the content and construction Vivino ratings and found that community comments and ratings express similar rich knowledge of wines as experts, wines are typically assessed within geographical regions and the analysis of website usage points to the absence of spam and/or trolls affecting the credibility of the ratings. This evidence points to some credibility for the community ratings data employed.…”

Section: Data and Empirical Specificationmentioning

confidence: 99%

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Community and Expert Wine Ratings and Prices

Oczkowski

Pawsey

2019

J of App Econs and Policy

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This paper assesses the relative impact community consumer ratings and expert ratings have on Australian wine prices. Results suggest community ratings may better explain price variations than expert scores; however, the performance difference is not definitive. Expert scores are found to have some additional unique power beyond community ratings in explaining prices. These results hold for both models based on current quality and reputation ratings and a number of robustness checks. The results suggest that producers may need to monitor both expert and community assessments of wines based on numerous vintages to accurately assess wine prices.

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Section: Data and Empirical Specificationmentioning

confidence: 99%

Section: Data and Empirical Specificationmentioning

confidence: 99%

Community and Expert Wine Ratings and Prices

Oczkowski

Pawsey

2019

J of App Econs and Policy

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“…In contrast, the study by Kotonya (2018) gives little evidence for a strong relationship between prices and consumer ratings. Nevertheless, both Kotonya’s (2018) and Oczkowski and Pawsey’s (2019) results suggest that expert opinions and online wine community reviews are related and often comparable. Such results have also been confirmed by a recent market experiment run by U.S. wine critic Ester Mobley on Vivino users from California in 2022.…”

Section: Introductionmentioning

confidence: 89%

“…Multiple studies support this claim (Oczkowski and Doucouliagos, 2015; Schamel and Anderson, 2003; Schamel and Ros, 2021), at least for high-rated “superstar” wines (Castriota et al, 2022). Demand for highly rated wines among wine producers has increased, causing an inflation of prices (Kotonya et al, 2018). Considering this, significant scrutiny has arisen regarding the validity of expert wine ratings as sensory evaluations are profoundly subjective, reflecting the taste preferences of just one individual (Oczkowski and Pawsey, 2019).…”

Section: Introductionmentioning

confidence: 99%

Factors influencing wine ratings in an online wine community: The case of Trentino–Alto Adige

Gastaldello,

Schäufele-Elbers,

Schamel

2024

J Wine Econ

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Consumers often struggle to make their choice in the highly diversified wine market. With wine being an experience good, consumers must rely on extrinsic characteristics, e.g., information on the label. Thus, easily available quality signals like consumer ratings have become an increasingly useful and widespread tool. Vivino is one of the largest online wine communities with over 60 million users, which have more than doubled since 2018. Hence, users have easy access to peer ratings, while established wine expert ratings are being challenged. This study analyzes data from Vivino to explore factors affecting consumer ratings at different price points, considering several wine attributes like geographical indications, brand, and the so-called “community effect.” We show that there is a small but significant community effect on wine's perceived quality related to its popularity among users of the Vivino community, as well as effects from specific wine attributes. Moreover, we estimate a hedonic quantile model on similar price ranges to compare the effect of the same regressors on wine prices. Results contribute to a better understanding of how different factors affect consumers’ wine evaluations, allowing to compare their effect on the “pure” consumer preference (i.e., consumer ratings) and market value.

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“…Previous studies on recommendation systems show that some researchers have analyzed some shared data from commercial platforms for wine and online games. The analysis of the SNS data from 9.2 million wine brands and 29.9 million reviews showed the tendency of user preference of wine in terms of the origin and age of the wine [7,8]. Although the tendency of user preference is useful for designing user models in practical recommendation systems, user behavior affecting the commercial effectiveness of the recommendation system remains unclarified.…”

Section: Introductionmentioning

confidence: 99%

A Case Study on the Effectiveness of Recommendation Al-gorithms for Sake Review Website

Hayama

Niiyama

2023

IIAI Letters on Institutional Research

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Recommendation systems have been used in various online businesses and have been recognized as useful for promoting product purchase and information browsing. However, the system design policies for each target service remain insufficiently clarified. One reason for this is that few case studies in practical environments exist since only the provider of the commercial platform obtains and confirms operational data of the recommendation algorithm and the resulting user behavior. Therefore, in this study, we investigate how some recommendation algorithms affect user behavior in online services for the Japanese Sake system. In the investigation, one of the four recommendation algorithms was assigned to each of the 858 users who logged into the system during 80 days. For each recommendation algorithm, the number of system logins, transitions to the sake description pages, sake brands registered in the sake to-drink list, and posted review comments to each sake brand were analyzed from the system history. Our results show that the recommendation system with association analysis was effective in recommending sake brands for its review sites, considering individual preferences and general popularity.

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Of Wines and Reviews: Measuring and Modeling the Vivino Wine Social Network

Cited by 9 publications

References 12 publications

Community and Expert Wine Ratings and Prices

Community and Expert Wine Ratings and Prices

Factors influencing wine ratings in an online wine community: The case of Trentino–Alto Adige

A Case Study on the Effectiveness of Recommendation Al-gorithms for Sake Review Website

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