Recommender systems encounter scaling challenges mainly because of their filtering implementations. The implementation of multiple evaluation factors analsys approaches in recommender systems causes identical scalability issues. The processing of extensive data quantities induces deterioration of system effectiveness. The proposed work expands prior research by focusing on enhancing scalability features for the multiple evaluation factors system. The system requires additional computer nodes through a scale-out approach for its execution. The recommendation production procedures ran on various computer systems which formed a cluster that operated using the Apache Spark platform. Tests measured the Apache Spark cluster processing time of the multiple evaluation factors filtering recommendation system to evaluate scalability while implementing speedup value comparisons. Performance metrics demonstrate that the application which operates on the Apache Spark achieves greater implementation speed than the standalone sequential version. As nodes in the cluster extend in number the system fails to achieve its theoretical maximum speedup value.