Research Of Online Recommendation Algorithm For Enterprise User

Currently, the recommendation algorithms in the e-commerce domain have been well developed, especially in collaborative filtering algorithms and the variety of recommendation algorithm derived from it. These recommendation algorithm is mainly used in B2C e-commerce system. But with the developing of applications, the number of business-to-business (B2B) e-commerce systems is highly increased, so the needs of the recommendation algorithm from B2B e-commerce system have become even more intense.The traditional collaborative filtering algorithm has many drawbacks. Cold start problem exists when the user firstly register and log in the system, and data sparseness problem occurs in some specific areas or customers. User-based collaborative filtering algorithm has a high time complexity, high cost on server. Project-based collaborative filtering algorithm can be processed offline, efficiency, low time cost and faster, but it needs the support of data warehousing. These bring a lot of trouble to B2B e-commerce systems. The appliance of recommendation algorithms to corporate users are also somewhat different from the algorithms used in B2C systems.In this paper, we designed and developed a leading B2B e-commerce system in certain domain which is Huamao barter exchange system. After deeply studied the collaborative filtering recommendation algorithm, compared to a variety of searching, sorting algorithm, we provide a online recommendation algorithm for corporate users for that system. The algorithm is committed to have high efficiency, high-quality and recommend the best selling and best products that meet the customer needs. The algorithm takes advantage of multi-level classification structure which is prevalent in e-commerce systems to quickly and accurately determine the nearest neighbor of the enterprise and goods involved in the transaction records. They can be used to mark out the products we want to recommend. The algorithm resolves the sparse data problem and cold-start problem. Through the effective establishment of the heap data structure, we can conveniently and efficiently sort the goods based on score results that successfully improved the efficiency of the algorithm.During the rapid development, the goods needed by businesses are constantly upgrading, the old product obviously does not meet the growing needs of businesses. So the algorithm abandon the transaction record which is long time ago. We only analyze the most recent transaction records, not only increase the efficiency of the algorithm, but also improve the quality of recommendations and customer satisfaction. The recommendation based on of the transaction log may bring a problem that the goods which is recommended may have been sold out. So the algorithm takes advantage of string similarity algorithm based on Levenshtein Distance to find the new product which is a replacement of the original product from the original publisher. That solves the problem of invalid recommendation.