  An architectural framework, based on collaborative filtering using K-nearest
neighbor and cosine similarity, was developed and implemented to fit the
requirements for the company DecorRaid. The aim of the paper is to test
different evaluation techniques within the environment to research the
recommender systems performance. Three perspectives were found relevant for
evaluating a recommender system in the specific environment, namely dataset,
system and user perspective. With these perspectives it was possible to gain a
broader view of the recommender systems performance. Online A/B split testing
was conducted to compare the performance of small adjustments to the RS and to
test the relevance of the evaluation techniques. Key factors are solving the
sparsity and cold start problem, where the suggestion is to research a hybrid
RS combining Content-based and CF based techniques.
