结合实时推荐与离线推荐的推荐系统

推荐系统是从大量信息中主动查找用户可能感兴趣的信息的工具.如何更好地贴近用户偏好，满足用户长期固有偏好的同时又能考虑到用户短期的兴趣焦点变化，是推荐系统长期研究的一个问题.此外，在对推荐系统进行设计时，为了提高推荐性能，除了专注于用户建模优化、推荐对象建模优化或推荐算法优化外，还需要将推荐系统作为一个整体进行系统性的研究，关注如系统流畅性、可伸缩能力等.针对这些问题，本文设计了一种实时推荐与离线推荐相结合的推荐系统，提出了采用待推荐池的方法保证系统的流畅性；在分析实时数据与历史数据的基础上，提供实时推荐与离线推荐，在贴合用户长期固有偏好的同时也能适应用户短时间内的兴趣焦点变化；采用控制模块对不同推荐结果数据进行控制调节，提高系统的可伸缩能力.基于该推荐系统，本文进行了对于微信文章的推荐实验，通过对待推荐池内数据进行分析来评价推荐效果，结果表明，推荐数据能够逐步贴近用户兴趣偏好.

Recommendation System Based on Real-Time Recommend and Offline Recommend

Recommendation system is a tool to automatically find information that users may be interested in from a large amount of information. How to get closer to users'' preferences, satisfy users'' long-term inherent preferences, and simultaneously take into account users'' short-term interest focus changes is an everlasting research problem of recommendation systems. In addition, in order to improve the recommended performance when designing the system, we not only focus on user modeling optimization, recommendation object modeling optimization or recommendation algorithm optimization, but also need to systematically study the recommendation system as a whole, focusing on system fluency and scalability. To solve these problems, this study designs a recommendation system that combines real-time recommendation with offline recommend, and proposes a method to ensure the fluency of the system by using the pool of recommendation data. Based on the analysis of real-time data and historical data, real-time recommendation and offline recommendation are provided, which can fit the long-term preferences of users and adapt to the recent change of interest focus. The control module of the system is used to control and adjust different recommendation result data to improve the scalability of the system. Based on the recommendation system, this study conducts a recommendation experiment for WeChat articles, and evaluates the recommendation effect by analyzing the data in the recommendation pool. The experimental results show that the recommendation data of the system can be gradually close to the user''s interest preference.