Personalized Product Recommendation for Users

This work deals with the issue of personalized recommendations to users in the domain of multimedia content, focusing on the adaptive selection of a recommendation method for a particular user or group of users. Conventional implementations of recommender systems use methods that are optimized for all users and can, due to diversity of users, limit the resulting accuracy of the recommendation. The quality of recommendations in the field commerce is key to product sales as well as user experience.

In this work, we focus on creating a hybrid recommendation method that would choose the most appropriate recommendation method for a user or group of users with similar characteristics to achieve qualitatively better results than to use a conventional recommendation method. In the domain of multimedia content, we specialize in movie recommendation by using a publicly available data set. In the course of this work we analyse the various recommendations techniques, state of the art, evaluation metrics and design and implementation of our own adaptive recommendation method based on the switching hybrid recommendation, including the selection of suitable attributes that will be the input for our method.