论文标题
基于优化的协作过滤算法的新型基于位置的VR在线购物推荐系统
A Novel Position-based VR Online Shopping Recommendation System based on Optimized Collaborative Filtering Algorithm
论文作者
论文摘要
本文提出了一个带有智能建议的VR超市，其中包括三个部分。 VR超市，推荐系统和数据库。 VR超市提供了一个360度虚拟环境，供用户通过VR设备在虚拟环境中移动和交互。建议系统将根据数据库中的数据向目标用户提出智能建议。智能推荐系统是根据项目相似性（ICF）开发的，该系统解决了ICF的冷启动问题。这使VR超市可以在任何情况下提出实时建议。它不仅弥补了传统在线购物系统中的用户对项目属性的看法，而且VR超市通过智能推荐系统提高了用户的购物效率。该应用程序可以扩展到企业级系统，这为用户在家中购物的新可能性增加了。
This paper proposes a VR supermarket with an intelligent recommendation, which consists of three parts. The VR supermarket, the recommendation system, and the database. The VR supermarket provides a 360-degree virtual environment for users to move and interact in the virtual environment through VR devices. The recommendation system will make intelligent recommendations to the target users based on the data in the database. The intelligent recommendation system is developed based on item similarity (ICF), which solves the cold start problem of ICF. This allows VR supermarkets to present real-time recommendations in any situation. It not only makes up for the lack of user perception of item attributes in traditional online shopping systems but also VR Supermarket improves the shopping efficiency of users through the intelligent recommendation system. The application can be extended to enterprise-level systems, which adds new possibilities for users to do VR shopping at home.
