Recommender systems have gained a lot of popularity in recent times dueto their application in the wide range of fields. Recommender systems areintended to support users in finding the relevant items based on their interestsand preferences. Recommender algorithms proposed by researchersevolved over time from simple matching recommendations to machine learningalgorithms. One such class of algorithms with increasing focus is oncalled streaming recommender systems, these algorithms treat input data asa stream of events and make recommendations. To evaluate the algorithmsthat work with continuous data streams, stream-based evaluation techniquesare needed. So far, less interest is shown in the research so far on the evaluationof recommender systems in streaming environments.In this thesis, a simple evaluation framework named StreamER that evaluatesrecommender algorithms that work on streaming data is proposed.StreamER is intended for the rapid prototyping and evaluation of incrementalalgorithms. StreamER is designed and implemented using object-orientedarchitecture to make it more flexible and expandable. StreamER can beconfigured via a configuration file, which can configure algorithms, metricsand other properties individually. StreamER has inbuilt support for calculatingaccuracy metrics, namely click-through rate, precision, and recall.The popular-seller and random recommender are two algorithms supportedout of the box with StreamER. Evaluation of StreamER is performed via acombination of hypothesis and manual evaluation. Results have matched theproposed hypothesis, thereby successfully evaluating the proposed frameworkStreamER.