Study on A Recommendation Algorithm of Crossing Ranking in E-commerce

Duan Xueying
2014 International Journal of u- and e- Service, Science and Technology  
The analysis of the existing recommendation system and the main task in the electronic commerce application and the existing problems of the basis, according to the new user "cold start" problem, to adopt a user in a number of different categories of electronic commerce website access multi-B2C behavior information recommendation. This paper presents a crossing ranking recommendation algorithm. Its accuracy can be far more than the random recommendation, at the same time keeping and diversity
more » ... re recommended. All these ensure the algorithm has a good user experience. Experiments show that the algorithm is accurate and the algorithm is further enhanced. complete absence of a website information record. This part uses the research of new ideas, and research the analysis object of new, its ideas and results have important value for the study of the existing e-commerce recommendation system. Data Analysis of Multi-B2C Behavior In order to verify the effectiveness of ranking recommendation algorithm and a recommendation system gets information for recommendation accuracy, this paper will use the percent mall e-commerce consumer behavior data platform, from a number of different time range of typical vertical B2C website data for empirical analysis, and in accordance with the study on the problem of different to the original the data into two different groups of datasets [5] . Percent mall currently has the most professional recommendation engine platform, is the largest span electric consumption data platform. The main business services model for individual commodities to the electronic commerce website recommendation service. Percent store data on the platform, using the Global ID to capture and record the user behavior trajectory information in a plurality of electronic commerce web-site, these include the user clicks on a product, the collection and purchase behavior. Even if the same user registration different accounts in different business website, using GID information can also identify the user global ID. The entire dataset is provided in a single or a plurality of electric business website user behavior information. In this study, we use multiple business data on the information platform [6] . The experiment data are derived from the B2C web site: peel network focus on skin care and beauty products sales network, many kinds of the goods covered including skin care products, cosmetics, perfume and health products. Wheat bag is a domestic large-scale direct online website bag, bags of multi brands its sales of goods including computer bag and bag included. Red child is currently the nation's largest maternal and child products vertical electric [7] [8] . The empirical analysis related to these sites, in the site scale and business scope are great differences.in addition, included the traditional form of B2C Internet marketing in the form, including popular form of network group purchase in recent years [9] . According to the different research, we will file the raw data into two different sets of data. This chapter is a part of the data for the user behavior information contained in the peel network, wheat bags and shoes base three site were selected in April 2012, Completing appropriate data preprocessing, and further divided into peel Network -wheat bags (referred to as G-M) and bread bag -the library name shoes (under referred to as M-S) of two independent datasets. This is an independent dataset that consists of two electric information in the composition is simple, it is known as the two electric dataset, it will be confirm the validity of the ranking recommendation algorithm. We also increased the electric quantity in October 2012, after screening, the wheat bags, red children, Yao point 100 and shows network user behavior information of 4 electric current months. And these four business sites are referred to as X1-MBB, X2-RB, X3-YD and X4-XT, this dataset called multi electric dataset. This information consists of second large groups of datasets to test algorithm, and to verify the effect of recommendation system gets information of recommendation accuracy [10-11]. Crossing Ranking Recommendation Algorithm of Multi-B2C Behavior The key point of crossing ranking recommendation algorithm of multi-B2C behavior, using ranking user multi-B2C behavior, to visit each other in the commodity business, equivalent mapping for the target business on certain commodities in access and give 60 Copyright ⓒ 2014 SERSC affect the accuracy of algorithm, and the site operation type will also have a certain impact on the algorithm. : In setting the size of the mapping table recommended = 10, the maximum number of resource allocation threshold = 5, the product of about 90% can be established in this manner recommended by the mapping table. Based on this foundation recommended mapping table, the four electric were cold start recommendation simulation. Using the recall rate and precision evaluation of the accuracy of Crank algorithm. At the same time compared with Grank algorithm and Rrank algorithm. In the limited space, select X3-YD and X4-XT two groups of simulation results are described in detail. The Accuracy Rate and Recall Rate Many to one in Figure 6 can be seen in the experimental accuracy recommended. First of all, from 1for1, 2for1 to the case of 3for1, the actual number of different types of electricity providers that we were increased from 1 to 3, gradually improve the accuracy of Crank algorithm. For example in the recall rate were 0.06, 0.09 and 0.14, eventually more than Grank algorithm in the same circumstances. This shows that with the increase in the amount of information recommended accuracy will be getting higher and higher. Secondly, the Grank algorithm for the three user recommendation accuracy is not consistent, and gradually decline. This trend shows the severe cross user (both behavior in multiple business users compared to nonusers) cross, for the popular goods aren't particularly interested in, which is reflected in reduced accuracy index. Virtually all the reality of the users can not only browse a single or a few sites, Users in a certain extent are severe cross users or potential users. With the increasing amount of information recommendation system access, the Grank algorithm this seemingly will get better, recommendation accuracy will not meet the real needs of users. We believe that the recommended system under real conditions can get beyond popular recommendation accuracy. Of course, the Crank algorithm will still maintain good novelty and diversity. Figure 6. Comparison of X3-YD Accuracy Rate and Recall Rate
doi:10.14257/ijunesst.2014.7.4.6 fatcat:vpdfnnlxqvebfcqi4z6xsaqkzu