A Hybrid Collaborative Filtering Based Recommender Model Using Modified Funk SVD Algorithm

Big data processing is a growing problem for information and communication systems. Based on the identification of features and relationships in the arrays of information, the most effective scenarios for further actions are determined. Examples of systems that use big data analysis to improve the quality of user service are the Industrial Internet of Things, smart cities, and intelligent commercial structures. Since smart production systems cover both the processes of direct production of items and their sale on the market, it is necessary to constantly monitor demand. Determining current offers for goods or services promotes the interest of customers in the products of the system. Algorithms and methods of big data processing in information communication systems were analyzed in the article. A comparative description of the main approaches to distributed collection, storage, and processing of information from different users was carried out. Features of functioning and basic requirements for the Industrial Internet of Things systems operation, the use of machine learning methods, and mathematical statistics to determine the main features for further data processing and rejection of redundancy were considered. The use of recommender systems to solve the problem of finding relationships between data was determined. A collaborative filtering model using the Funk SVD method for more efficient data processing was proposed. The Funk SVD algorithm was modified using the neighborhood-based collaborative filtering method for the possibility of calculating fewer data and reducing the duration of the processing while maintaining the accuracy of the result. A simulation of the operation of the modified algorithm was carried out, the results of which demonstrate high speed and accuracy compared to the unmodified one. Using distributed Fed SVD algorithm to improve the efficiency of recommender systems, aggregate calculations, and reduce the load on one device was proposed. It was determined that the results of the research and the proposed algorithms can be used to create a hybrid recommender system that calculates big data, flexibly determining the optimal parameters of work.