THE METHODS OF DATA STORING OF A RECOMMENDATION SYSTEM BASED ON LINKED LISTS

Abstract

The goal of this work is to research and comparative analysis of methods and data structures for storing information of a recommendation system in order to compare the effectiveness of their use in terms of time and memory costs. The choice of the method for presenting the data used by the recommendation system is important since an effective way of building a database for the operation of such a system can reduce the amount of resources required and increase the number of available algorithms for generating lists of recommendations, and is also important from the point of view of the quality of its work, speed, scalability, and ease of performing basic operations with data to generate recommendations. The presence of a large number of different methods for implementing databases and presenting information that can be used to build recommendation systems necessitates a comparative analysis and selection of the optimal method and data structure for storing information in them. In the work, research was carried out of various data structures that can be used to store information of the recommender system. In particular, such as linked list, unrolled linked list, hash table, B-tree, B+-tree and binary decision diagram. To carry out experiments comparing the effectiveness of using various data structures in terms of time and memory costs, a computer model of a simplified recommendation system was developed, in which three main entities were distinguished - an agent, a session, and an object. The best results were obtained with data storing methods using unrolled and inverted unrolled linked lists. Therefore, it was decided to also conduct an additional series of experiments with these data structures for different sizes of the list bl ock. The unrolled list showed the best results in terms of memory used and session generation time. The inverted unrolled list showed an advantage in the generation of recommendations.