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.

翻译：本文开发并实现了一种基于K近邻和余弦相似度的协同过滤架构框架，以满足DecorRaid公司的需求。旨在通过在该环境中测试不同评估技术，研究推荐系统的性能。针对该特定环境中的推荐系统评估，本文确定了三个相关视角：数据集视角、系统视角与用户视角。基于这些视角，能够更全面地评估推荐系统的性能。通过在线A/B分离测试，比较推荐系统微小调整的表现，并验证评估技术的相关性。解决稀疏性和冷启动问题是关键因素，建议研究结合基于内容与协同过滤技术的混合推荐系统。