Recommender System

The recommender system makes it possible for retailers to start offering products and brands to customers individually, relying on information about the customer’s previous behavior and accumulated statistical data.

Recommender systems are part of a personalization trend in retail business. There are basically two approaches to defining a recommendation: the collaborative approach, which evaluates behavior of other customers, and the feature-based approach, which compares product features with available information about the customer. BitRefine group offers a hybrid of these two approaches.

  • The collaborative part of recommender system is universal for different types of retailers. It needs no extensive knowledge about product features, but it does require a big dataset of user behavior.
  • The feature-based part requires as many properties of each product as possible to be able to distinguish one product from another and find the right one for each particular customer.

Recommender systems have mostly become popular in online business. There are also great opportunities for such services in traditional offline retail. Offering products that suit a customer’s profile helps to improve customer experience, increases turnover, and plays the role of soft advertisement. There are several options for offline implementation of such a system. However, the crucial requirement for this type of system is still its relevance. On the basis of collected expertise BitRefine group builds an individual solution for each particular case. Each retailer has a different product range, different business approach, and different data available for processing. Thus, we believe, only an individualized approach can build a highly relevant recommender system.

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