家居装饰推荐系统中的协同过滤、K近邻与余弦相似度
Collaborative filtering, K-nearest neighbor and cosine similarity in home decor recommender systems
AI总结：
本文为DecorRaid公司开发了基于协同过滤、K近邻和余弦相似度的推荐系统框架，通过多视角评估和在线A/B测试，提出解决稀疏性和冷启动问题的混合推荐系统方案。
摘要：
本文开发并实现了一个基于协同过滤、K近邻和余弦相似度的架构框架，以满足公司DecorRaid的需求。论文旨在测试该环境中的不同评估技术，以研究推荐系统的性能。在特定环境中，评估推荐系统有三个相关视角，即数据集视角、系统视角和用户视角。通过这些视角，可以更全面地了解推荐系统的性能。进行了在线A/B分割测试，以比较对推荐系统进行小幅调整后的性能，并测试评估技术的相关性。关键因素在于解决稀疏性和冷启动问题，建议研究结合基于内容和基于协同过滤技术的混合推荐系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
