An integrated fuzzy adjusted cosine similarity and TOPSIS based recommendation system for information system requirements selection
Faiz Akram, Tanvir Ahmad, Mohd Sadiq
Abstract
Selecting the requirements for an information system based on their ranking values is one of the critical steps in software development. Various methods for computing the ranking order of the requirements have been developed using multi-criteria decision-making methods. This study shows selecting the requirements for different software releases using a recommendation system is an unresearched problem. We present a recommendation system (PRecSys) for computing the ranking order of the requirements in an information system using fuzzy-based adjusted cosine similarity measures and collaborative filtering to recommend the requirements to stakeholders with similar interests. The ranking order of the information systems requirements in PRecSys is computed using the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The applicability of PRecSys is discussed by considering the requirements for a library information system. The PRecSys is compared with PHandler, an expert system for choosing the requirements, based on the following criteria: agreement measure, the number of judgments by decision-makers, and time complexity.