The rapid development of e-commerce has led to a swiftly increasing number of competing providers in electronic markets, which maintain their own, individual data describing the offered items. Recommender systems are popular and powerful tools relying on this data to guide users to their individually best item choice. Literature suggests that data quality of item content data has substantial influence on recommendation quality. Thereby, the dimension completeness is expected to be particularly important. Herein resides a considerable chance to improve recommendation quality by increasing completeness via extending an item content data set with an additional data set of the same domain. This paper therefore proposes a procedure for such a systematic data extension and analyzes effects of the procedure regarding items, content and users based on real-world data sets from four leading web portals. The evaluation results suggest that the proposed procedure is indeed effective in enabling improved recommendation quality.

电子商务的飞速发展导致电子市场中竞争性提供商的数量迅速增加，这些提供商保持着自己的描述所提供商品的个人数据。推荐系统是流行且功能强大的工具，它依赖于此数据来指导用户选择各自最佳的项目。文献表明，项目内容数据的数据质量对推荐质量具有重大影响。因此，尺寸的完整性有望特别重要。在此，存在通过将项目内容数据集扩展为同一域的附加数据集来提高完整性来提高推荐质量的巨大机会。因此，本文为这种系统的数据扩展提出了一种程序，并分析了该程序对项目的影响，内容和用户基于来自四个领先Web门户的真实数据集。评估结果表明，所提出的程序确实可以有效提高建议质量。