Research And Application Of Personalized Recommender Algorithm In R2C Business Platform

In recent years, with the rapid development of mobile internet and the coming age of big data, the penetration rate of intelligent terminals is rising year by year, and various types of E-commerce platforms are also in constant development. Many distinctive platforms have emerged. Especially, the e-commerce platform on intelligent terminals has received a great deal of attention. Among these numerous patterns, B2C (Business-to-Customer) and C2C (Customer-to-Customer) are two kinds of mature modes that have been widely used in many e-commerce enterprises. Currently,O2O is a hot new business mode. As customer needs become more diversified, a lot of new e-commerce platforms also have been developed. Meanwhile, the demand of delivering products is generated, so the distribution system for e-shopping is rising. This paper focuses on the distribution system for e-shopping over short and middle distance. R2C (Region-to-Customer) mode which proposes in the paper is a newly emerged mobile consumer platform, and it is an extended form by integrating B2C mode and C2C mode. R2C mode is a kind of community platform and has broad application scenarios in the future. In the era of information explosion, how to efficiently recommend useful message to users on R2C platform is the research emphasis in this paper. At present, the personalized recommendation algorithm based on LBS (Location Based Service) on mobile recommender system is an important research direction, and R2C mobile commerce platform can collect a lot of location information. The theory of recommending personalized location on R2C e-commence platform can be used in real life to achieve specific business value.The main contents of this paper:1) it describes the development of R2C e-commerce platform and the prospect of applying recommender algorithm on R2C e-commerce platform.2) It analyzes traditional recommender algorithms and introduces related theories and concepts mentioned in this article.3) It introduces research status and application fields of personalized location recommender algorithms, and then describes a famous location recommender algorithm---SG algorithm. After that, it studies the basic algorithm of two important algorithms in this paper--- Location category Recommendation Based on Periodicity of Human Activities. 4) It describes the research emphasis in detail:introduces Location recommendation based on periodicity of human activities and collaborative filtering recommendation based on periodicity of human activities; summarizes the algorithm proposed in the paper, which names location scoring-periodic area recommendation system, and introduces the advantage of the algorithm when applying it to R2C e-commerce platform.5) It compares the proposed method with some existing location recommender algorithms by doing comparison experiments. And experimental results demonstrate the proposed two algorithms based on location category recommender algorithm, which the method of recommender system, which is based on the human activity cycle are more suitable on R2C platform, and perform better. 6) In addition, it prospects the development of e-commerce platform. As it studies the development and extension of existing e-commerce platforms, it has positive significance for the development of intelligent community, as well as the practical significance for people to get safer and faster shopping.