A distributed group recommendation system based on extreme gradient boosting and big data technologies

摘要

Personalized recommendation systems have emerged as useful tools for recommending the appropriate items to individual users. However, in such situations, some items tend to be consumed by groups of users, such as tourist attractions or television programs. With this purpose in mind, Group Recommender Systems (GRSs) are tailored to help groups of users to find suitable items according to their preferences and needs. In general, these systems often confront the sparsity problem, which negatively affects their efficiency. With the increase in the number of users, items, groups, and ratings in the system. Data becomes too big to be processed efficiently by traditional systems. Thus, there is an increasing need for distributed recommendation approaches able to manage the issues related to Big Data and sparsity problem. In this paper, we propose a distributed group recommendation system, which is designed based on Apache Spark to handle large-scale data. It integrates a novel proposed recommendation method, a dimension reduction technique, with supervised and unsupervised learning for dealing efficiently with the curse of dimensionality problem, detecting the groups of users, and improving the prediction quality. Experimental results on three real-world data sets show that our proposal is significantly better than other competitors.