Product Recommendation by Means of Data Analysis

A challenge of searching for the most relevant items for clients has always been relevant in the area of retail services. The internet technologies have made it necessary to automate the process of generating patterns within transactions. The challenge of composing N recommended items based on one previously chosen item is studied in this paper. A new hybrid model that incorporates such methods as searching through frequent itemset mining, items with conditional probability and through the most popular items is presented in the paper. The influence of the model parameters on the quality metric is investigated. Moreover, the model was compared with aforementioned methods separately with respect to the quality of recommendations and the performance of algorithm. According the results of the study the hybrid model is proved to achieve better performance and quality then other methods.