Item-based recommender system with statistical learning for unauthorized customers

This article aims to reveal that using statistical learning approaches for recommender systems better build personal communication with consumers than does expert opinion regarding this question. Cosine similarity distance was used as a basis for developing machine learning recommendation model. However, this distance has a high cost of calculation, and ways of resolving this problem were considered. The matrix of the probability of purchasing one item with another was calculated in order to weight cosine similarity and avoid the situation when unpopular products are put in the top of recommendation. Weighted sum model was applied in order to join cosine similarity and probability matrices and build recommendation sequences. User-based collaborative filtering is the most popular algorithm to build personal recommendation, but it useless when it is impossible to identify a user in the system. A developed algorithm based on cosine similarity distances, probability matrix and weighted sums allows building item-to-item recommendation model. The main idea of this approach is to offer additional products to clients when only products in a basket are known. Item-to-item recommendation algorithm has shown advantages of using statistical machine learning approaches in order to improve communication with clients through mobile application and website. An integrated recommendation module has revealed that developing a data-driven culture is the right way of many modern companies.