基于现实社区综合因素的社区化智能推荐算法研究

Research on Community-Based Intelligent Recommendation Algorithm Based on Real Community Comprehensive Factors

随着电子商务的不断发展,个性化推荐算法得到了广泛应用。针对现今多数智能推荐算法并没有真正定位到个体居民之间联系的问题,本文提出了基于现实因素的社区化智能推荐算法。首先引入一般线性模型结合高斯–马尔科夫定理搭建用户模型,实时刻画用户画像,判断用户的实际期望价值。随后引入口碑公式利用现实因素对外部口碑的优化特点搭建社区模型并与用户模型相融合,从而在提升个人价值期望的同时对商品产生忠诚度。通过推荐算法,可以间接地将用户与算法相粘合,将现实模式下的优势带入虚拟,使用户获得极大的感知度,在用户间架起一座沟通的桥梁,具有极大的应用前景。

With the continuous development of e-commerce, personalized recommendation algorithms have been widely used. Aiming at the problem that most intelligent recommendation algorithms do not really locate the connection between individual residents, this paper proposes a community-based intelligent recommendation algorithm based on practical factors. Firstly, the general linear model is introduced combined with the Gauss-Markov theorem to build a user model, depict the user portrait in real time, and judge the actual expected value of the user. Then, the word-of-mouth formula is introduced, using the optimization characteristics of real factors on external word-of-mouth to build a community model and integrate it with the user model, so as to enhance personal value expectations and generate loyalty to the product. Through the recommendation algorithm, the user can be indirectly bonded with the algorithm, and the advantages in the real mode can be brought into the virtual, so that the user can obtain great perception, build a bridge of communication between users, and have great application prospects.