Recommendation Engine: AI-supported into the future

Recommendation engines – the advantages at a glance

1. Improved personalization

AI algorithms can analyze huge amounts of data to identify individual preferences and behavioural patterns. This enables the creation of highly personalized recommendations, which increases customer loyalty and sales.

2. Increased relevance

While traditional systems do not recognize complex relationships between products and users, AI-supported engines can. This makes recommendations more precise and relevant.

3. Real-time adaptation

AI models can adapt to changing user preferences in real time, e.g. in the context of seasonal trends or current events.

4. Discovery of new products

By analyzing user data, AI can recommend products that the customer might not otherwise have found. This promotes both cross-selling and up-selling.

It’s a match: Autoencoder

For an AI recommendation engine to meet the requirements, a suitable solution is needed. This is where autoencoders come into play – a special type of machine learning. In the case of recommendation engines, autoencoders must learn to compress and restore data efficiently. The focus here is on retaining the most important information in compressed form without losing important details.

For a tangible visualization: you have a sheet of paper that you fold up as small as possible. At first glance, only the now small paper is perceived, but when it is unfolded again, the sheet is still there in its entirety.

The aim of the process is to play out improved product recommendations. This is made possible by autoencoders that recognize patterns and correlations in large data sets.

Autoencoder – advantages at a glance

1. Efficiency

Autoencoders can be trained with less computing power than competing AI models, making them a cost-effective option.

2. Scalability

Autoencoders can be easily applied to large data sets. This makes them the ideal solution for growing e-commerce businesses.

3. Flexibility

Autoencoders can be adapted to different types of data. Examples include product information, user reviews and demographic data.
Autoencoder – advantages at a glance

Autoencoder in use: The two approaches

Content-based filtering:

In this approach, the autoencoder analyzes the characteristics of the products and uses this to learn to identify similar products, which are ultimately played out to people individually.

Collaborative filtering:

The user’s behavior is the basis here. This is analyzed so that users with similar preferences can be identified. This serves as a template for recommendations.

Challenge: New users

Both content-based and collaborative filtering face the so-called “cold start problem”. This refers to the fact that new users or products do not yet have a history on which the AI can rely. Hybrid approaches that combine different methods are used to solve this problem.

Content-based recommendations for new users

Based on initial information or the first behavior of users, content-based recommendations can be generated first until sufficient data is available for collaborative filtering.

Popularity-based recommendations

For new products, the most popular items in the respective category are recommended first.

Combined approaches

By combining content-based and collaborative filtering, known cold-start problems can already be solved.

Recommendation engines with Medienwerft

As an e-commerce agency with expertise in the development of individual and data protection-compliant AI solutions hosted on our own servers in Germany, we support you in the implementation of AI-supported recommendation engines that are tailored to your needs.

We analyze your data, develop tailor-made autoencoder models and integrate the recommendation engine seamlessly into your e-commerce platform.