A survey on ordered weighted averaging operators and their application in recommender systems

Abstract

Recommender Systems (RS) are turned into remarkable tools in electronics commerce (e-commerce) in a way that they effectively find items which are suitable for user's interests. Techniques such as collaborative filtering and content-based filtering are designed for RS. One of the novel methods to recommend appropriate items is using the Ordered Weighted Averaging (OWA) operators to fuzzify the output of RS [1]. OWA is one of the decision-making methods capable of considering the priorities and mental evaluations of a decision-maker. Furthermore it has the ability to assess the measure of orness and include the computation in final decision. This article aims at presenting methods that have been proposed to combine RS and OWA operators and also at proposing the implementation and development of these two methods in future.