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|Target know you're pregnant: generating mechanism behind personalized recommendation
i world network operators NOTE: The field of electronic business, most notably the recommendation system should belong to the Amazon online bookstore. \u0026 Ldquo; a book of the same readers who are interested in a way similar interests \u0026 rdquo; the assumption that provide such recommendation has become Cheap Nike Free Outlet a model for the electricity supplier recommendation system under the premise of today. And behind this, the main mechanism involved is WEB mining and collaborative filtering analysis. The latter by analyzing the user interested in finding in the Nike Air Max 2011 user base is similar to the specified user (interested) users, the combination of these similar information of a user evaluation, forming system for the specified user for this information to predict the degree of preference. Such a recommendation algorithm will not like traditional direct analysis of content filtering based on content Nike Air Max 2012 recommendation, maybe you no longer have to worry about on this site search casket of electricity supplier. Text / Topchoice information chief operating officer Tan Lei all remember the Target case: In 2012, the Womens Heels United States, a man broke into a retail supermarket chain Target protest near his home: You even gave me a 17-year-old daughter send baby diapers and stroller coupons. Store manager always immediately acknowledged the error, but the manager did not know this behavior is headquarters to run data mining and personalized recommendation results. A month later, the father came to apologize, because then he knows his daughter is indeed pregnant. Target than the father knew his daughter pregnant a full month earlier. Target's case is based on the results of data mining user behavior analysis done, and after analysis, system users Womens Nike Kobe VIII a personalized recommendation, so that they are sure to provide customers with merchandise that they like and want. Personalized Recommendation To understand the principle behind personalized Nike Air Max 1 (Womens) recommendation, come to understand the Internet mining and collaborative filtering these two concepts. Rather, the Internet's New Nike Air Max 2015 Women Mining (WEB Mining) refers to the use of data mining technology to automatically discover from the documents on the Internet and Internet services in and extract the information that people are interested in. Web mining is an important support for the modern e-commerce strategies, especially web mining user Nike Blazers High access patterns mining is mainly used for customer analysis and to identify potential customers information online behavior. An implementation Web mining is the server logs, error logs and message logs, the local terminal data log file for analysis, to dig out access behavior of users, access frequency Nike Blazers Low and browse the contents of other information, in order to identify certain patterns Nike Shoes Global and rules. This leads to the concept of collaborative filtering. Collaborative filtering is a technique for information retrieval, the goal is to help users find the content of interest in a flood of Internet information (merchandise) in. In the application of e-commerce, we usually use collaborative filtering technology to identify related product recommendations. This product recommendation method is called recommender system or personalized recommendation system. In plain Women Lebron Hyperdunk 2013 words, collaborative filtering algorithm Nike Shoes can help you find with a similar group of people like to see what they buy, and then recommend to you. The main method of collaborative filtering has the following three ways: based on the user: to collect user information Nike AIr Max Women's properties. For each user A, find, and he is quite close (or similar) several users. Using these similar user-to-point interest to predict user A, and the potential of those points of interest has not been excavated recommended to the user A. Based on the project: collecting items of information property. For a user, if he has a high interest in the project X, then he Air Jordan Outlet is likely to have Nike Air Jordan XX8 on the X similar items are potentially interested. Content-based: In addition to user and project attributes, content-based collaborative filtering will further analyze the user's evaluation of the content and feedback. A project such as user interested in X, but his interest in X is \u0026 ldquo; \u0026 rdquo ;, hated him and so as not to be able to \u0026 ldquo; like \u0026 Nike Air Max 2011 Women rdquo; Project X users classified together. Find similarities, of course, in the field of electronic commerce, the project mainly refers to the commodity. Collaborative filtering recommendation is based on the assumption: If Nike Free some users for some items scores similar to those of other users like you and Nike Air Max 2011 Men users of these Women Nike Air Yeezy II items scores are relatively similar, and these users and other items made by similar for these items The scores were also similar. Let's illustrate: electronic mall you bought a movie disc 'Lord of the Rings' and 'Star Wars', collaborative filtering algorithm can help Nike SB Shoes you find other people to buy similar products. If the algorithm found that they also buy a 'Harry \u0026 middot; Nike Air Max Thea Print Men Potter', will put 'Harry \u0026 middot; Potter' recommend to you. If you bought Yu Hua's 'Brothers' and 'alive', Su Tong's 'Raise the Red Lantern', the system found other people to buy these books and found that these people bought Mo Yan's 'Sandalwood Penalty', so will this book recommend to you. When we find and customers A \u0026 ldquo; like \u0026 rdquo; after Nike Air Max 2012 Men those other customers, you can sort the list of goods, the more customers and clients A similar buy a commodity X, it shows commodity X is more likely to attract customers A. Simply put, if we think there are 1000 customers and clients A \u0026 ldquo; like \u0026 rdquo;, then historical data can be transferred out of these customers purchased Nike Basketball according to the amount of order. If one of 100 customers have purchased merchandise X, there are 90 customers to buy the merchandise Y, then we will give priority to customer A recommended product X, then Y. Featured Products Personalized Recommended Recommended system under large data, or as personalized recommendation system is based on a system of data mining on the basis that the customer shopping offer fully personalized decision support and information service for the purpose. Almost all overseas large e-commerce sites, including Amazon and eBay, Nike Free Run Shoes have varying degrees of use of various Nike Running forms of recommendation systems. We look at a rule-based collaborative filtering recommendation system implementation case. As following the table below, Chris, Jenny, Mark and Peter are all users in the system, while commodity A, B, C, etc. are users purchased goods, Womens Nike KD VI tick indicates that the corresponding user purchases a corresponding product. Let's look at a user-based recommendation. Presumably through recommendation system, we find similarity Chris and Peter two users is very high, which Peter has purchased a commodity A, C, E, and and he was \u0026 ldquo; like \u0026 rdquo; of Chris just buy product A, Recommended system will recommend the goods C and E to Womens Nike LeBron 11 Chris. Let us look on the recommended projects. Presumably through recommendation system, we find commodities and commodity D B is \u0026 ldquo; like \u0026 rdquo; of. Many users of the Nike Lunar system, Jenny purchased goods B and D, Tina made a purchase D, and Venessa a purchase B. Our recommendation system based on rules similar goods B and D, will commodity B recommended to Tina, and the goods D Tell Venessa. In collaborative filtering recommendation system, a factor to consider is the recommended commodities X and Y itself Nike Blazers purchase frequency. If 10% of customers buy the merchandise X, while only 1% of customers buy the commodity Y, so then we should probably recommended product is Y instead of X. In the All users We can add a factor in the sort of time, and the value of the coefficient is the commodity and the overall heat inversely proportional. Because commodity X in all client proportion purchased commodity Y 10 times, and in and customers A \u0026 ldquo; like \u0026 rdquo; the proportion of the population is buying more than just 10 percent, then we sort of needed to Y row X on the front. It should be added that the collaborative filtering recommendation system is dependent on large amounts of data. If the data is not sufficient, it is recommended results may ironic. In Nike Air Max the new user, the new project or the entire system is a case of a new beginning, personalized recommendation engine is not working. 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