A Consumer Recommendation System based on Big Data

Aiming at the field of e-commerce, this paper analyzes e-commerce user behavior based on the data characteristics of e-commerce back-end logs and constructs a user behavior mining model. On the basis of Web user behavior theory, it analyzes user behavior based on interactive content. Based on the background of big data, the traditional data mining algorithm is further optimized, which greatly improves the operating efficiency of the algorithm. At the same time, a distributed file storage structure is adopted to improve the fault tolerance of system data processing. This paper studies the advantages and disadvantages of collaborative filtering recommendation algorithms. The Web user behavior mining system constructed in this paper can conduct multi-dimensional and efficient mining. It helps e-commerce merchants and content providers to understand their users and achieve better commercial value through precise marketing and accurate recommendations, and complete the upgrade of data-driven services.

Section: Demand Analysismentioning

confidence: 99%

Research on User Access Pattern Mining Based on Web Log

Wu¹

2020

“…It is essentially an inductive process. The purpose of data analysis in customer relationship management is to discover hidden information in the data [13][14][15]. With the changing business needs of enterprises, the continuous updating of customer data, and the optimization of technology development systems, it is necessary to constantly adjust the established data model and to repeatedly carry out data mining to adapt to the requirements of enterprises [16] [17].…”

Section: Application Process Of Customer Relationship Management Undementioning

confidence: 99%

Research on Customer Relationship Management Based on Data Mining

Kunasekaran¹,

Developers²,

Faculties³

et al. 2020

This paper studies the application of customer relationship management based on data mining. Through the use of data mining technology to mine all aspects of customer relationship management, scientifically and orderly manage customer relationships, help companies reduce and avoid risks, and improve company profits. This article takes the mall customer relationship management system as an example, uses the SPRINT classification algorithm to classify customers, improves the accuracy of customer classification, and makes the mall's related decisions more credible. The best customer retention strategy algorithm is used in customer retention to help the shopping center make decisions, so as better to retain customers and maximize the profit of the shopping center.

“…Regarding the research of social networks, the current research is mainly carried out from the perspective of communication and sociology. After long-term exploration, researchers have found that social networks also have the convenience of applying big data [11] and authenticity [12][13][14]. Social network data is born big data.…”

Section: The Value Of Social Network Big Data To Personal Credit Repomentioning

confidence: 99%

Influence of Internet-based Social Big Data on Personal Credit Reporting

Li¹,

Zhang²,

Wang³

2020

With the advent of the era of big data, the connotation of Credit investigation in China has gradually shifted from traditional data to social big data. The sources of credit data are not limited to traditional data, such as credit data; It also includes data on network behavior based on Internet transactions. The scene of credit investigation also extends from the financial industry to economic and social life. Based on the traditional credit analysis model, this paper puts forward a new network behavior credit analysis model by replacing "mortgage" with "social". Through data collection, data model, data service and other links, this paper proves that big data of network behavior can cover all credit investigation elements and can be used as a data source for personal credit analysis, with very high integrity. Among them, social network big data is a very important data source and occupies a high weight. This paper adopts the information research method, collects, processes and collates the social network big data, personal credit investigation and other information, constructs the "5C" credit analysis method of network behavior, and studies the personal credit investigation of the social network big data by combining the examples of Domestic Internet giants Tencent and Ali, and adopts the tabling method and cobweb map, etc. The network behavior credit analysis model proposed in this paper makes full use of the big data of social network to promote the development of personal credit investigation business and provides meaningful and valuable Suggestions for the government and industry.