工商管理代寫Essay要求：Predective Analytics in E-commerce Application
本文是工商管理專業的Essay范例，題目是“Predective Analytics in E-commerce Application（電子商務應用中的前瞻性分析）”，摘要:本文概述了機器學習和預測分析的原理。它解釋了大數據的基本原理和不斷發展的電子商務技術。近年來，網絡上的業務增長迅猛。
世界各地的組織都意識到，電子商務不再只是通過互聯網進行購買或銷售，而更多的是關于用戶體驗的動態性和互動性。分析顧客行為，組織將能夠提高銷售和留住顧客。在電子商務組織中，有幾種客戶行為可以通過預測分析來預測。從你想要預測的結果開始是很重要的。本文的重點是跟蹤客戶的購買行為，通過在電子商務平臺上向客戶推薦商品來提高銷售。通過一個案例研究，我們將了解如何使用python構建推薦系統。在本研究中，我們提出了一個智能的推薦系統，向顧客推薦商品。
Predective Analytics in E-commerce Application電子商務應用中的前瞻性分析
Abstract—This paper outlines the principal of machine learning and predictive analytics. It explains the fundamentals of big data and evolving E-commerce technology. Business over web has observed tremendous growth during recent times. Organizations around the globe are realizing that E-commerce is just no more about buying or selling over the internet, rather it’s more about how dynamic and interactive a user experience can get. Analyzing customers behavior, the organization will be able to improve sales and retain customers. There are several customer behaviours which can be predicted using predictive analytics in an E-commerce organisation. It is important to start with the outcomes that you would like to predict. This paper focuses on tracking customers purchasing behaviour to improve sales by recommending items to customers on an E-commerce platform. A case study has been presented in which we are going to see how to build recommender system using python. In this study we present an intelligent recommendation system to suggest items to customer.
I.Introduction介紹
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. 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.
在當今的電子商務時代，預測分析技術扮演著非常重要的角色。預測分析有幾種方法可以幫助組織成長，重要的是要分類哪些使用與您的業務相關，并選擇通過分析預期目標可以創造最大機會的領域。你可以考慮增加公司收入，檢測欺詐行為，優化客戶服務，成本效益技術，洞察客戶行為。一旦選擇了合適的目標，預測分析可以為在線零售商產生巨大的競爭優勢。雖然有一些限制，例如模型需要在實現之前進行質量檢查，并且需要進一步的人工干預來維護和運行模型，但是優點大于缺點。在電子商務中使用預測分析有許多優點，一旦部署，就可以立即觀察到好處。以下是一些領先的趨勢，它們正在走向當今商業的前沿。推薦引擎與Netflix和亞馬遜使用的推薦引擎類似，它利用過去的購買行為向消費者推薦新的購買行為。預測市場戰略的風險引擎，新產品創新的創新引擎，客戶洞察引擎和復雜操作和決策的優化引擎。今天，我們還處在將預測分析應用于解決現實問題的冰山一角。預測分析方法釋放了數據的力量。簡而言之，這種方法允許我們預測未來。數據科學算法可以毫不費力地預測誰會在不久的將來購買、欺騙、撒謊或死亡。
II. Introduction to Predictive modelling
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.
A.Ensemble of statistical algorithms統計算法集成
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.
統計學對于理解數據很重要。它告訴我們大量的數據。數據是如何分布的?中心的變化是小的還是大的?統計數據幫助我們回答這些問題。另一方面，算法是模型的藍圖。他們負責從歷史數據中創建數學方程。他們分析數據，量化變量之間的關系，并將其轉化為數學方程。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:有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 dataHistorical數據
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.
通常，模型建立在歷史數據的基礎上，對未來數據進行處理。此外，預測模型可以通過對稀疏的歷史數據進行插值來填補歷史數據中的缺失值。在建模過程中，未來數據是不可用的，因此在采樣中使用歷史數據作為未來數據。
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.
III.Recommender System
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). 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.
IV.Examples of recommender system
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.
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.
C.eBay
The Feedback Profile feature at eBay.comallows 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.
eBay.com的反饋檔案功能允許買家和賣家貢獻反饋檔案的其他客戶，他們已經做了業務。反饋包括滿意度評分(滿意/中性/不滿意)以及對其他客戶的具體評論。反饋是用來為買家提供一個推薦系統，買家可以查看賣家的資料。賣家簡介包括過去幾年銷售的歷史評級，所有賣家的反饋和評論都可以提供給客戶。
V. Case study
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.
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
在這個案例研究中，我們將看到如何在python中實現推薦的項目。為了向顧客推薦同類人帶來的產品。在本例中，我們將使用有關哪些客戶帶來了哪些產品的數據，并基于這些數據構建一個項目對項目的親和度得分，然后使用它向客戶推薦產品。下面是一個包含UserId和ItemId的數據文件
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.
VI.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. Ryan Aminollahi
在接下來的案例研究中，我們可以基于客戶的購買行為構建一個簡單的推薦系統。我們考慮了item和用戶數據，找到了親和性得分，以便向客戶推薦產品。對于5001，我們看到5002和5005的分數是。4,5004是。2,5003是0。我們可以進一步對閾值進行分類，超過這個閾值，我們將推薦一些項目。例如，我們將只推薦分數在。25以上的產品，然后我們將向客戶推薦5002和5005產品。瑞安Aminollahi
VII. Conclusion and future of recommendation system
Current state of technology
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. Sofiya Mujawar, former Data Scientist at Big Data Solutions
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，推薦系統可以應用到更廣泛的方面，包括日常生活問題。推薦系統可以解決日常生活問題，推薦日常活動和飲食習慣。它提供功能來跟蹤營養消費，并說服用戶以積極的方式改變他們的飲食行為。Web服務尤其容易受到向數百萬用戶推薦數百萬項內容的影響。時間和計算能力甚至會限制最佳混合系統的性能。對于較大的數據集，我們可以研究推薦系統的可擴展性問題。
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