With the continuous improvement of people's income and living standards, as well as the constant pace of people's work and life, more and more people go shopping in the supermarket. In order to meet people's fast shopping needs, most supermarkets now add a large variety of goods for people to choose. However, it is followed by a large number of products in different positions, causing customers to find difficulties, time-consuming and laborious, affecting customer satisfaction and loyalty to the business, thereby affecting the sales of the merchant. With the advent and maturity of RFID and collaborative filtering technologies, an RFID-based collaborative filtering technology enables supermarkets or other businesses to offer customers a personalized solution. 1.RFID technology RFID is the abbreviation of Radio Frequency IdenTIficaTIon System (Radio Frequency Identification System). It is a non-contact automatic identification technology that uses radio waves to read and write recording media with high reliability and confidentiality. 2. Simple working principle of RFID RFID is a non-contact automatic identification technology that automatically identifies target objects and acquires relevant data information through RF signals. RFID systems generally consist of tags, readers, application interfaces or middleware software, transport networks, business applications and management systems. The specific RFID works as follows: When the tag enters the magnetic field, it receives the RF signal from the reader, and sends the product information (passive tag or passive tag) stored in the chip by the energy obtained by the induced current, or actively transmits a signal of a certain frequency (there is Source tag or active tag), the reader reads the information and decodes it, and sends it to the central information system for data processing. 1, collaborative filtering technology Collaborative filtering technology is to analyze the user interest, find the similar (interest) users of the specified users in the user group, synthesize the evaluation of certain information by these similar users, and form a prediction of the system's preference for the information of the designated users. Technology. 2. Principle of collaborative filtering technology The principle of collaborative filtering is that users with similar interests may be interested in the same thing. So as long as the data about the user's preferences is maintained, the user with similar tastes can be analyzed and then recommended to him based on the opinions of similar users. Another possible starting point is that users may prefer products that are similar to what they have purchased. The degree of similarity between the products can be judged based on the user's evaluation of various things, and then those items that are closest to the user's interest are recommended. [1] The implementation of collaborative filtering is generally divided into two steps: first, obtain user information, that is, obtain user's evaluation of certain items (commodities); secondly, use the corresponding collaborative filtering technology algorithm to analyze the similarity between users and predict the target users to some The preference of a project (commodity). 3. Classification of collaborative filtering technology algorithms At present, according to the difference of the starting point of the collaborative filtering algorithm, it can be divided into a collaborative filtering algorithm based on customer-customer relationship and a collaborative filtering algorithm based on project (commodity)-item (commodity) relationship. Collaborative filtering algorithm based on customer-customer relationship [2]. The "nearest neighbor algorithm" is by far the most successful automatic recommendation technique. This technique uses statistical methods to select the users most similar to the target users, called "neighbors", and then guess the target based on the opinions of these neighbors or previously purchased products. The degree to which the user is interested in the target item. Apply it to the recommendation system, first cluster according to the preference records of each user, each type of user has similar interest habits. Once the clustering is complete, the user can be recommended based on the common preferences of all members of the class. Collaborative filtering algorithm based on project-project relationship [3]. The collaborative filtering algorithm based on project (commodity)-project (commodity) relationship first focuses on the relationship between projects (goods). The algorithm looks at the set of items (goods) that all target customers have evaluated, calculates the degree of similarity between these items (goods) and the item (commodity) i that is being considered, and picks out the closest k items (commodities) {i1 , i2,..., ik}, the corresponding degree of similarity is {Si1, Si2, ..., Sik}. A weighted average of the scores of the target customers for these similar items (commodities) is then calculated, ie the desired predicted values ​​are obtained. According to the above analysis, the RFID-based collaborative filtering technology can be applied to solve the problem that the supermarket is difficult to quickly obtain the goods that it needs in the face of a large number of products. The specific ideas are as follows: If the supermarket sets a card with an RFID tag for each in-store customer, the card stores the basic information of the customer, such as the customer's ID number, name, personal hobby, and the like. The system obtains the customer ID through the reader when the customer enters the supermarket, and the information is sent to the supermarket computer information system, and by querying the supermarket database, if the customer has previously purchased the goods in the supermarket, the customer's past shopping situation and preferences can be obtained, and Combining the personal information in the customer's RFID card, the collaborative filtering technology is used to obtain the products purchased and loved by the customers with similar preferences, and the personalized product recommendation list is opened for the customer, so as to provide better service to the customer in a targeted manner. . In addition, it is also possible to recommend other commodities that may be of interest according to the similarity of the products according to the merchandise purchased by the customer, thereby facilitating the purchase of the customer and promoting the sales. If the customer is a new customer or has not previously made a purchase in the supermarket, there is no shopping record in the database, then the collaborative filtering technology can be used to reason according to the personal information in the customer's RFID card, and the customer who has similar preferences can purchase and love. The products provide customers with personalized product introductions, so as to provide better services to customers in a targeted manner. As the number of customer purchases increases, the system can obtain new customer preferences based on the preferences set by the customer and the items that the customer frequently purchases, and use this as the basis for the next customer referral. In short, the relationship between supermarkets and customers is the most important part of each supermarket, and it is also the key to the profitability of supermarkets. Using RFID technology and collaborative filtering technology, we can cater to customers' tastes as much as possible, improve customer satisfaction, and ensure that the supermarket gets the most profit. At present, collaborative filtering technology is mainly applied in the field of B2C e-commerce. Due to China's national conditions and other factors, most consumers still use the traditional way of shopping in supermarkets or shopping malls. Therefore, this paper adopts RFID technology and collaborative filtering. The combination of technology and application in traditional supermarket shopping has a good application prospect.
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Traditional supermarket shopping application based on RFID technology and collaborative filtering technology
First, the problems encountered by customers when shopping in the supermarket