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              電子商務Essay范文翻譯:Predective Analytics in E-commerce Application

              論文價格: 免費 時間:2022-06-27 11:01:52 來源:www.eanhe.com 作者:留學作業網

              本文是電子商務專業的留學生Essay范例,題目是“Predective Analytics in E-commerce Application(電子商務應用中的預測分析)”。在當今這一代在線商務中,預測分析技術起著至關重要的作用。 預測分析可以通過多種方式幫助組織發展,重要的是要對與您的業務相關的用途進行分類,并通過分析所需目標來選擇將創造最大機會的領域。 您可以考慮增加公司收入、檢測欺詐、優化客戶服務、具有成本效益的技術、客戶行為洞察力。 一旦選擇了合適的目標,預測分析就可以為在線零售商帶來巨大的競爭優勢。

              In today’s generation of online commerce, predictive analytics technology plays very crucial role. There are several ways with which predictive analytics can help an organization to grow, it is important to categorize which use is relevant to your business and pick the area that will create the maximum opportunity by analyzing the desired targets. You may consider increasing the company revenue, detection of fraud, optimizing customer service, cost effective techniques, customer behavior insights. Once the appropriate target is selected predictive analytics can generate huge competitive advantage for an online retailer.

              電子商務Essay怎么寫

              Though there are few limitations, for instance models need to undergo quality check before implementation and further human intervention is necessary to maintain and run the model, however advantages outweigh the drawbacks. There are numerous advantages for using predictive analytics in E-commerce and once deployed, benefits are observed instantly. Here are some leading trends that are making their ways to the forefront of the business today. Recommendation engines similar to those used in Netflix and Amazon uses past purchases and buying behavior to recommend new purchases to consumers. Risk engines to forecast market strategy, innovation engines for new product innovation, customer insight engines and optimization engines for complex operation and decision making. Today we are at the tip of iceberg in terms of applying predictive analytics to solve real world problems. Predictive analytics approach unleashed the might of the data. In short, this approach allows us to predict the future. Data science algorithms can effortlessly predict who will buy, cheat, lie, or die in the near future.

              Introduction to Predictive modelling預測模型概述

              Predictive modelling is an ensemble of statistical algorithms coded in a statistical tool, which when applied on historical data, outputs a mathematical function or equation. It can in turn be used to predict outcomes based on some inputs (on which the model operates) from the future to drive a business context or enable better decision making in general. Predictive modelling continues to generate great deal of interest in recent generation. (Konnie L. Wescott, R. Joe Brandon, 1999, 6). To understand what predictive modelling is, let us focus on terms highlighted previously.

              預測建模是在統計工具中編碼的統計算法的集合,當應用于歷史數據時,會輸出數學函數或方程。 反過來,它可以用于根據未來的一些輸入(模型在其上運行)來預測結果,以推動業務環境或總體上實現更好的決策。 預測建模在最近一代中繼續引起極大的興趣。 (Konnie L. Wescott, R. Joe Brandon, 1999, 6)。 要了解什么是預測建模,讓我們關注之前強調的術語。

              A.    Ensemble of statistical algorithms

              Statistics are important to understand data. It tells volumes about data. How is the data distributed? Is it centered with little variance or it varies widely? Statistics helps us answer these questions. Algorithms, on the other hand are the blueprints of a model. They are responsible for creating, mathematical equations from historical data. They analyze the data, quantify the relations between the variables and convert it in to a mathematical equation. There are variety of them: Linear regression, logistics regression, clustering, decision trees, natural language processing and so on. These models can be classified under two classes: Supervised algorithms and unsupervised algorithms.

              統計數據對于理解數據很重要。 它告訴有關數據的卷。 數據是如何分布的? 它的中心是變化很小還是變化很大? 統計數據可以幫助我們回答這些問題。 另一方面,算法是模型的藍圖。 他們負責根據歷史數據創建數學方程。 他們分析數據,量化變量之間的關系并將其轉換為數學方程。 它們有很多種:線性回歸、邏輯回歸、聚類、決策樹、自然語言處理等。 這些模型可以分為兩類:監督算法和無監督算法。

              Supervised algorithms: These are the algorithms wherein the historical data, an output variable in additional to the input variables. The model makes use of the output from historical data, apart from the input variables. The example of such algorithms includes Linear regression, Logistic Regression Decision Trees and so on.

              Unsupervised algorithm: These algorithm work without an output variable in the historical data. The examples of such algorithm include clustering.

              B.    Historical data

              In general, model is built on historical data and works on the future data, Additionally, a predictive model can be used to fill the missing values in historical data by interpolating the model over sparse historical data. During modelling future data is unavailable hence historical data is used in sampling to act as future data.

              C.    Mathematical function

              Most of the data science algorithms have underlying mathematics behind them. In many of the algorithms, such as regression, equation is assumed and parameters are derived by fitting the data to the equation.

              D.   Business context

              All the effort that goes into predictive analytics and all the worth, which accrues to data, is because it solves a business problem. Business problems can be anything and varies widely.

              As discussed earlier, predictive modelling is and interdisciplinary field sitting at the interface and requiring knowledge of four disciplines such as statistics, algorithms, tools, techniques and business sense.

              Recommender System推薦系統

              Recommender systems are widely used in the e-commerce market for personalized and unique recommendations of other products for each customer.” In a world where a site’s competitors are only a click or two away, gaining customer loyalty is an essential business strategy” (Reichheld and Sesser, 1990) (Reichheld, 1993) The recommended products can be anything for example physical goods, films, music, articles, social tags and services. The system enriches the online experience, increases the conversion rate and affects the revenues positively (Schafer, Konstan and Riedl, 1999). Theoretically, recommender systems are a “spectrum of systems describing any system that provides individualization of the recommendation results and leads to a procedure that helps users in a personalized way to interesting or useful objects in a large space of possible options”(Lampropoulus and Tsihrintzis 2015, p.1).

              推薦系統廣泛用于電子商務市場,為每個客戶提供個性化和獨特的其他產品推薦?!?在一個網站的競爭對手只有一兩下的距離的世界里,獲得客戶忠誠度是一項基本的商業戰略”(Reichheld 和 Sesser,1990 年)(Reichheld,1993 年)推薦的產品可以是任何東西,例如實物商品、電影、音樂 、文章、社交標簽和服務。 該系統豐富了在線體驗,提高了轉化率并對收入產生了積極影響(Schafer、Konstan 和 Riedl,1999)。 從理論上講,推薦系統是“描述任何系統的系統譜,這些系統提供個性化的推薦結果,并導致一個程序,以個性化的方式幫助用戶在大量可能的選項空間中找到有趣或有用的對象”(Lampropoulus 和 Tsihrintzis 2015 ,第 1 頁)。

              A recommender system helps its user by filtering an overload of information by providing the most appropriate and valuable information for the specific user. To make recommendations, personal information about the user preference is required in order to predict the user’s rating for other items than they have been in touch before. There are three different methods of collecting knowledge about user preferences: implicit, explicit and mixing approach. The implicit approach does not require any active involvement from the user and is based on recording the user behavior. A typical example of implicit rating is a historic purchase data. The explicit approach is based on user interrogation by requiring the user to specify their preference for any particular item. Lastly, the mixing approach is a combination of the previous two. There are two main approaches of designing a recommender system: content-based methods and collaborative methods. By assuming that a user’s preferences remain unchanged through time, one can predict their future actions based on past user behaviors. In other words, all the information stored about the user will be used to customize the services offered. While, the main assumption for collaborative filtering is that similar users prefer similar items. This method relies entirely on interest ratings from the users and can be categorized into two different branches: model-based and memory-based. The model-based algorithms use statistical and machine-learning techniques to make predictions based on the underlying data. The memory-based methods can be further divided into two classes: user-based and item-based. User-based collaborative systems make user-user similarity calculations by matching the user against a database of other users who have similar interests. Items that the other users have bought but unknown to the specific user are offered as a recommendation for the specific user. The item-based collaborative system is, on other hand, based on matching a specific item to a database of other items. Thus, this approach is based on item relations rather than user relations and makes the final prediction based on similarities between items which have been rated by a common user.

              In order to build a recommender system to recommend products to the customer we will be using collaborative filtering. Collaborative filtering works on just three pieces of data. A user or a customer, an item, and an affinity score between the user and the item.

              電子商務Essay范文翻譯

              Examples of recommender system推薦系統舉例

              In this section we will see few of the reputed E-commerce companies that utilize one or more variations of recommender system technology in their web sites.

              在本節中,我們將看到很少有知名電子商務公司在其網站中使用一種或多種推薦系統技術的變體。

              A.    Amazon.com

              Amazon uses the recommender system in many aspects, Amazon videos, Amazon Appstore, Amazon logistics, web page recommendations, customer and seller services. Let’s see how Amazon uses each aspect in detail.

              In books, Amazon used customer who brought feature. This feature is found on the information page for each book  in the catalog. The first recommends books frequently purchased by customers who purchased the selected book. The second recommends authors whose books are frequently purchased by customers who purchased works by the author of the selected book.

              B.    Netflix

              More than 80 percent of the TV show people watch on Netflix are discovered through the platform’s recommendation system. That means the majority of what you decide to watch on Netflix is the result of decision made by machine learning and algorithm. Netflix uses machine learning and algorithms to help break viewers preconceived notion and find shows that they might not have initially chosen.

              人們在 Netflix 上觀看的電視節目中有 80% 以上是通過該平臺的推薦系統發現的。 這意味著您決定在 Netflix 上觀看的大部分內容都是機器學習和算法做出的決定的結果。 Netflix 使用機器學習和算法來幫助打破觀眾的先入為主的觀念,并找到他們最初可能沒有選擇的節目。

              C.    eBay

              The Feedback Profile feature at eBay.com? (www.ebay.com) allows both buyers and sellers to contribute to feedback profiles of other customers with whom they have done business. The feedback consists of a satisfaction rating (satisfied/neutral/dissatisfied) as well as a specific comment about the other customer. Feedback is used to provide a recommender system for purchasers, who are able to view the profile of sellers. The seller profile consists of historical rating from the sales made in past years and all the seller feedback and reviews are available for the customer.

              Case study案例研究

              Let’s take an example of person purchasing a laptop from a E-commerce website. Addition to laptop one might need charging pads, mouse and additional warranties for damage. Knowledge of the customer’s purchasing desires and situations will create upsell and cross sell opportunities for the companies to sell the product and make some quick profits from the data available.

              讓我們以一個人從電子商務網站購買筆記本電腦為例。 除了筆記本電腦,可能還需要充電板、鼠標和額外的損壞保修。 了解客戶的購買意愿和情況將為公司創造追加銷售和交叉銷售的機會,以銷售產品并從可用數據中快速獲利。

              Up-sell means selling additional items in the same category along with the main motivational purchase. Cross-sell relates to selling addition items in different categories that the customer might desire.

              If a person purchases a high end laptop, the person might be further interested in purchasing a high end game, gaming accessories, hard disk, router, antivirus software or Microsoft office suit. There are a few factors we might want to consider to determine the cross and upsell opportunities related to particular customer.

              If we can predict such events, related or desired products can be recommended to customer.

              In this case study we are going to see how to implement recommended items in python. In order to recommend the product to customer which similar people brought. In this case we will use data about which customer brought which products and based on that build an item to item affinity score and then use it to recommend items to customer. Here is a data file which includes the UserId and ItemId.

              The data file meant for representation consists of user ID and item ID. From the data we can see the use 1001 has purchased items 5001, 5002 and 5005. To extract information, we will load the file on jupyter notebook and build an affinity score between items based on users who purchased them.We are going to find affinity of every item to other item and the way I’m going to do it is by finding out how many customers have bought both these products. The higher the customers who has brought the items, the higher is going to be the affinity score.

              Once the affinity scores between each item have been printed. We see here Item 1 to 2 has a high affinity score of .4, whereas 5,001 to 5,003, there is no affinity at all.

              In this list of affinity score, in order to recommend items to customer, we are going to go back to this table, go to all the records that are item one in the first column, and get the list of all the items two and their scores. And we can do that in descending order. And those items that you see here is what I want to recommend. Let’s further see how we can use the affinity scores to know which products can be recommended to customer 50001.

              Results結果

              In the following case study, we were able to construct a simple recommender system based on customers purchasing behavior. We have taken in to consideration the item and user data to find the affinity score so that products can be recommended to customers. So for 5001, we see that 5002 and 5005 has a score of .4, of 5004 has .2, and 5003 has zero. We can further classify a threshold limit above which we will recommend items. For example, we are going to only recommend those items whose score is above a .25, then we would recommend the products 5,002 and 5,005 to the customer.

              在以下案例研究中,我們能夠構建一個基于客戶購買行為的簡單推薦系統。 我們已經考慮了項目和用戶數據來找到親和力分數,以便可以向客戶推薦產品。 所以對于 5001,我們看到 5002 和 5005 的得分為 0.4,5004 的得分為 0.2,而 5003 的得分為零。 我們可以進一步分類閾值限制,超過該閾值我們將推薦項目。 例如,我們將只推薦那些分數高于 0.25 的商品,那么我們將向客戶推薦產品 5,002 和 5,005。

              https://towardsdatascience.com/predictive-customer-analytics-part-iv-ab15843c8c63

              Conclusion and future of recommendation system推薦系統的結論與未來

              The industry is trying to integrate various recommender system which works on Point of interest or meta data or group recommendations. Every system is built according to the requirements of the organization.

              該行業正在嘗試集成各種適用于興趣點或元數據或組推薦的推薦系統。 每個系統都是根據組織的要求構建的。

              In my opinion the recommender systems can be applied to ever more broader aspects which includes daily life issue. Recommender systems can be applied to solve daily life issue and recommend curse of the day, which includes day to day activity and food habits. Which provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Web services in particular suffer from producing recommendations of millions of items to millions of users. The time and computational power can even limit the performance of the best hybrid systems. For larger dataset, we can work on scalability problems of recommendation systems.

              在我看來,推薦系統可以應用于更廣泛的方面,包括日常生活問題。 推薦系統可用于解決日常生活問題并推薦當天的詛咒,包括日?;顒雍惋嬍沉晳T。 它提供了跟蹤營養消耗以及說服用戶以積極的方式改變他們的飲食行為的功能。 Web 服務尤其受到向數百萬用戶生成數百萬個項目的推薦的困擾。 時間和計算能力甚至會限制最佳混合系統的性能。 對于更大的數據集,我們可以解決推薦系統的可擴展性問題。

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