One example of the use of this

Transformer models have already become the basis for many modern services, such as LinkedIn, Netflix and Spotify. They allow you to personalize recommendations, create individual offers and predict user needs.  technology is GPT chat, which is a generative model based on transformers. In fact, all of these services use this very technology, which was proposed by Google in the form of the Bert product.

Sputnik is also actively implementing

Transformative models in its solutions. In particular, two years ago they presented a new version of the recommendation algorithm based on GPT.  taiwan mobile database This was the most noticeable improvement in the company’s 12 years of existence, and the results were not long in coming.

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Impressive results thanks to new algorithms

In different industries, transformative models have different effects, but they always increase efficiency. For example, in the electronics sector, the increase in sales from the recommendation block  austria business phone List reached +40%. Other sectors, such as tools, showed an increase of +20%, and in the fashion and accessories industry, sales through recommendations increased threefold! Even pet products show a twofold increase in sales thanks to recommendation blocks.

What is a trnsformer model and how does it differ from traditional ones?

The transformer model, unlike traditional algorithms, is able to take into account the context. It can understand the logic and sequence of relationships between products. While the traditional model simply “ why must we secure data in transit? sees” product views and is based on this, the transformer model analyzes the overall context.

For example, if a traditional model sees that a user has viewed a bike, water bottle, and sports gloves, it will simply suggest additional sports products. A transformative model will understand that the user is a cyclist and suggest a helmet or special clothing.

Features of transformer models

1. Adaptability to heterogeneous data. Transformers take into account different types of data, such as product views, likes, and reviews, which significantly improves the accuracy of recommendations.
2. Easy integration of new product categories. The model automatically adapts to new categories, without the need to intervene in business logic.
3. Context processing. The model is able to take into account the sequence of events and their connection, which allows for more accurate recommendations.

One classic example of the benefits of transformers is a “cold start.” While a traditional model takes some time to adapt to new products, a transformer model quickly picks up on new data. For example, new sneakers with an ID tag that was not previously available will be taken into account and offered to users much faster.