Summary: Wine apps like Vivino and Hello Vino are utilizing AI algorithms to help wine enthusiasts select the perfect bottle.
Researchers have taken it a step further by incorporating people’s flavor impressions into the algorithms. Through wine tastings and data from wine labels and user reviews, they developed an algorithm that can make highly accurate predictions of individual wine preferences.
This innovative approach has the potential to revolutionize how we choose not only wines but also beer, coffee, and even personalized food recommendations.
Key Facts:
- Wine apps are using AI algorithms to assist users in selecting wines based on labels and reviews.
- Researchers integrated people’s flavor impressions into the algorithms for more accurate wine recommendations.
- This approach can be extended to beer, coffee, and personalized food recommendations, benefiting various industries.
Source: University of Copenhagen
For non-connoisseurs, picking out a bottle of wine can be challenging when scanning an array of unfamiliar labels on the shop shelf. What does it taste like? What was the last one I bought that tasted so good?
Here, wine apps like Vivino, Hello Vino, Wine Searcher and a host of others can help. Apps like these let wine buyers scan bottle labels and get information about a particular wine and read the reviews of others. These apps build upon artificially intelligent algorithms.
Now, scientists from the Technical University of Denmark (DTU), the University of Copenhagen and Caltech have shown that you can add a new parameter to the algorithms that makes it easier to find a precise match for your own taste buds: Namely, people’s impressions of flavour.
“We have demonstrated that, by feeding an algorithm with data consisting of people’s flavour impressions, the algorithm can make more accurate predictions of what kind of wine we individually prefer,” says Thoranna Bender, a graduate student at DTU who conducted the study under the auspices of the Pioneer Centre for AI at the University of Copenhagen.
More accurate predictions of people’s favourite wines
The researchers held wine tastings during which 256 participants were asked to arrange shot-sized cups of different wines on a piece of A3 paper based upon which wines they thought tasted most similarly. The greater the distance between the cups, the greater the difference in their flavour. The method is widely used in consumer tests. The researchers then digitized the points on the sheets of paper by photographing them.
The data collected from the wine tastings was then combined with hundreds of thousands of wine labels and user reviews provided to the researchers by Vivino, a global wine app and marketplace. Next, the researchers developed an algorithm based on the enormous data set.
“The dimension of flavour that we created in the model provides us with information about which wines are similar in taste and which are not. So, for example, I can stand with my favourite bottle of wine and say: I would like to know which wine is most similar to it in taste – or both in taste and price,” says Thoranna Bender.
Professor and co-author Serge Belongie from the Department of Computer Science, who heads the Pioneer Centre for AI at the University of Copenhagen, adds:
“We can see that when the algorithm combines the data from wine labels and reviews with the data from the wine tastings, it makes more accurate predictions of people’s wine preferences than when it only uses the traditional types of data in the form of images and text. So, teaching machines to use human sensory experiences results in better algorithms that benefit the user.”
Can also be used for beer and coffee
According to Serge Belongie, there is a growing trend in machine learning of using so-called multimodal data, which usually consists of a combination of images, text and sound. Using taste or other sensory inputs as data sources is entirely new. And it has great potential – e.g., in the food sector. Belongie states:
“Understanding taste is a key aspect of food science and essential for achieving healthy, sustainable food production. But the use of AI in this context remains very much in its infancy. This project shows the power of using human-based inputs in artificial intelligence, and I predict that the results will spur more research at the intersection of food science and AI.”
Thoranna Bender points out that the researchers’ method can easily be transferred to other types of food and drink as well:
“We’ve chosen wine as a case, but the same method can just as well be applied to beer and coffee. For example, the approach can be used to recommend products and perhaps even food recipes to people. And if we can better understand the taste similarities in food, we can also use it in the healthcare sector to put together meals that meet with the tastes and nutritional needs of patients. It might even be used to develop foods tailored to different taste profiles.”
The researchers have published their data on an open server and can be used for free.
“We hope that someone out there will want to build upon our data. I’ve already fielded requests from people who have additional data that they would like to include in our dataset. I think that’s really cool,” concludes Thoranna Bender.
About this artificial intelligence research news
Author: Maria Hornbek
Source: University of Copenhagen
Contact: Maria Hornbek – University of Copenhagen
Image: The image is credited to Neuroscience News
Original Research: Closed access.
“Learning to Taste: A Multimodal Wine Dataset” by Serge Belongie et al. Arxiv
Abstract
Learning to Taste: A Multimodal Wine Dataset
We present WineSensed, a large multimodal wine dataset for studying the relations between visual perception, language, and flavor.
The dataset encompasses 897k images of wine labels and 824k reviews of wines curated from the Vivino platform. It has over 350k unique vintages, annotated with year, region, rating, alcohol percentage, price, and grape composition.
We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment with 256 participants who were asked to rank wines based on their similarity in flavor, resulting in more than 5k pairwise flavor distances.
We propose a low-dimensional concept embedding algorithm that combines human experience with automatic machine similarity kernels.
We demonstrate that this shared concept embedding space improves upon separate embedding spaces for coarse flavor classification (alcohol percentage, country, grape, price, rating) and aligns with the intricate human perception of flavor.
