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Sentient Recommender

Recommend your customers what truly matters to them

Machine Learning

Made Simple

Intelligent interpretation of customer preferences

Quickly expanding product ranges and increasing individual choice options make it harder to pinpoint the personal taste of the consumer. The Sentient Recommender dives deep into that personal taste by analyzing the customer’s choices and recommending products that best fit their personal preferences. Recommender bases its insight in these personal preferences on a database that contains earlier choices by many other consumers.

Using a Touch Phone


Conventional product recommendations are based on historical lending and sales data. “Customers that have borrowed this book have also looked at these titles”. The Sentient Recommender goes further than this, by interpreting the personal taste based on the unique combination of preferences. In this process, the popularity of choices plays an important role. Some choices are based on how well-known the products are, rather than the actual personal preference of the consumer.

When, for instance, someone picks Madonna, U2 and Beth Hart as their favorites, the inclusion of Beth Hart in these is more interesting than the other two, as this choice is less obvious. Most people would probably have included Madonna and U2, whereas hardly anyone knows Beth Hart.

Recommender gives more weight to these particular choices when interpreting the customer’s personal taste.

The Future

Proven solution

The Sentient Recommender is being used in a musical recommendation service called Muziekweb, operated by the largest European music library based in Rotterdam. Also, Recommender is used in a similar service for books called Romanadvies, operated by the public libraries in the Netherlands. Through these services, many thousands of book and music lovers have already received recommendations tailored to their personal needs.

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