为了满足家居设计的个性化需求，将个性化推荐算法引入移动交互式三维家居个性
化设计系统中，提出了基于交互历史的协同过滤推荐算法. 首先，分析了用户的历史交互行
为，构建了一个新的用户兴趣度量度模型，将用户的交互行为转换成用户兴趣度矩阵. 然后，
综合考虑了家具单品的时效性和资源关联特性，并将这两种特性引入协同过滤推荐算法的生
成推荐过程中，以提高推荐质量. 最后，将系统推荐的家具单品应用到三维虚拟家居设计场景
中，通过对场景个性化编辑与虚拟展示，完成家居的个性化设计. 实验结果表明，该方法是可
行的，能有效提高推荐质量，具有较好的用户体验和视觉效果.

To meet individual needs of home design, a collaborative filtering recommendation
algorithm based on interaction history is presented in this paper. The algorithm has
been introduced into a mobile interactive 3D home design system. In the algorithm, user
historical interaction behavior is analyzed, and a new user interest model built. The model
converts user interaction behavior into a user interest matrix. To improve the recommendation
quality, two furniture item characteristics, i.e., timeliness and resource relevance,
are introduced into the generation process of the collaborative filtering recommendation
algorithm. The recommended items are applied to 3D virtual home design scene. The
home personalized design is completed by scene editing and virtual display. Experimental
results show that the proposed method is feasible. It can effectively improve recommendation
quality with better user experience and visual effects.