本文是电子商务专业的留学生Essay范例，题目是“Predective Analytics in E-commerce Application（电子商务应用中的预测分析）”。在当今这一代在线商务中，预测分析技术起着至关重要的作用。 预测分析可以通过多种方式帮助组织发展，重要的是要对与您的业务相关的用途进行分类，并通过分析所需目标来选择将创造最大机会的领域。 您可以考虑增加公司收入、检测欺诈、优化客户服务、具有成本效益的技术、客户行为洞察力。 一旦选择了合适的目标，预测分析就可以为在线零售商带来巨大的竞争优势。
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.
Introduction to Predictive modelling预测模型概述
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.
预测建模是在统计工具中编码的统计算法的集合，当应用于历史数据时，会输出数学函数或方程。 反过来，它可以用于根据未来的一些输入（模型在其上运行）来预测结果，以推动业务环境或总体上实现更好的决策。 预测建模在最近一代中继续引起极大的兴趣。 (Konnie L. Wescott, R. Joe Brandon, 1999, 6)。 要了解什么是预测建模，让我们关注之前强调的术语。
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.
统计数据对于理解数据很重要。 它告诉有关数据的卷。 数据是如何分布的？ 它的中心是变化很小还是变化很大？ 统计数据可以帮助我们回答这些问题。 另一方面，算法是模型的蓝图。 他们负责根据历史数据创建数学方程。 他们分析数据，量化变量之间的关系并将其转换为数学方程。 它们有很多种：线性回归、逻辑回归、聚类、决策树、自然语言处理等。 这些模型可以分为两类：监督算法和无监督算法。
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推荐系统
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).
推荐系统广泛用于电子商务市场，为每个客户提供个性化和独特的其他产品推荐。” 在一个网站的竞争对手只有一两下的距离的世界里，获得客户忠诚度是一项基本的商业战略”（Reichheld 和 Sesser，1990 年）（Reichheld，1993 年）推荐的产品可以是任何东西，例如实物商品、电影、音乐 、文章、社交标签和服务。 该系统丰富了在线体验，提高了转化率并对收入产生了积极影响（Schafer、Konstan 和 Riedl，1999）。 从理论上讲，推荐系统是“描述任何系统的系统谱，这些系统提供个性化的推荐结果，并导致一个程序，以个性化的方式帮助用户在大量可能的选项空间中找到有趣或有用的对象”（Lampropoulus 和 Tsihrintzis 2015 ，第 1 页）。
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.
Examples of recommender system推荐系统举例
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.
在本节中，我们将看到很少有知名电子商务公司在其网站中使用一种或多种推荐系统技术的变体。
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.
人们在 Netflix 上观看的电视节目中有 80% 以上是通过该平台的推荐系统发现的。 这意味着您决定在 Netflix 上观看的大部分内容都是机器学习和算法做出的决定的结果。 Netflix 使用机器学习和算法来帮助打破观众的先入为主的观念，并找到他们最初可能没有选择的节目。
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.
让我们以一个人从电子商务网站购买笔记本电脑为例。 除了笔记本电脑，可能还需要充电板、鼠标和额外的损坏保修。 了解客户的购买意愿和情况将为公司创造追加销售和交叉销售的机会，以销售产品并从可用数据中快速获利。
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结果
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，我们能够构建一个基于客户购买行为的简单推荐系统。 我们已经考虑了项目和用户数据来找到亲和力分数，以便可以向客户推荐产品。 所以对于 5001，我们看到 5002 和 5005 的得分为 0.4，5004 的得分为 0.2，而 5003 的得分为零。 我们可以进一步分类阈值限制，超过该阈值我们将推荐项目。 例如，我们将只推荐那些分数高于 0.25 的商品，那么我们将向客户推荐产品 5,002 和 5,005。
https://towardsdatascience.com/predictive-customer-analytics-part-iv-ab15843c8c63
Conclusion and future of recommendation system推荐系统的结论与未来
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.
该行业正在尝试集成各种适用于兴趣点或元数据或组推荐的推荐系统。 每个系统都是根据组织的要求构建的。
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.
在我看来，推荐系统可以应用于更广泛的方面，包括日常生活问题。 推荐系统可用于解决日常生活问题并推荐当天的诅咒，包括日常活动和饮食习惯。 它提供了跟踪营养消耗以及说服用户以积极的方式改变他们的饮食行为的功能。 Web 服务尤其受到向数百万用户生成数百万个项目的推荐的困扰。 时间和计算能力甚至会限制最佳混合系统的性能。 对于更大的数据集，我们可以解决推荐系统的可扩展性问题。
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