统计与数据科学学院知识库

      当前，互联网数据规模急剧扩大，我们已坐拥海量信息，可真正找到对自己有用信息的效率变得越来越低，且目前电商平台面临的一大难题就是如何快速、准确的为用户找到合适的商品，提升用户的购物体验感。而个性化推荐服务就是应对这一难题的有力工具，它不仅能为用户带来优质的服务，而且能够为商家带来前所未有的利润。
  电商平台中的用户行为都是有意义的，且其蕴含着无限的价值。因此，本文对阿里平台用户行为从时间演化、行为转化、行为时间间隔和复购情况等方面进行分析，发现浏览后用户流失率高且购买转化率低，以及其他一些用户行为特征。为了能够满足不同用户的个性化需求，提升用户的购物体验感，减少用户流失提高购买转化率，为商家创造更大的价值，本文针对电商平台的个性化推荐进行了研究。
  本文介绍了三种常用的推荐方法，对比分析了它们的优缺点及其适用场景发现基于内容的推荐方法比较适用于文本类推荐领域，基于关联规则的推荐方法主要被用来发现购物车之间的关联性，基于协同过滤的方法推荐的个性化程度较高，可以挖掘用户的潜在需求，而且可解释性强。针对电商平台，为了依据用户行为数据完成用户的个性化推荐，本文最终选择了基于用户的 协同过滤推荐方法。但同时也发现协同过滤推荐算法存在数据稀疏性问题、冷启动问题和扩展性等问题。对此，本文将协同过滤推荐算法与k-means聚类算法相结合来进行商品荐并将其与传统的协同过滤推荐算法做实验对比。实验结果表明：对于电商平台，基于 k-means聚类的协同过滤推荐算法推荐的准确率、召回率和 F1值均优于传统的协同过滤推荐算法，且其计算复杂度也较低，在缓解数据稀疏性问题的同时也有效解决了扩展性问题 不论是在推荐性能还是推荐效率上都表现出更大的优势这为电商平台的个性化推荐服务提供了一定的参考 。

      At present, the scale of Internet data is rapidly expanding. We are already sitting on massive amounts of information. The efficiency of finding useful information for ourselves has become lower and lower. At present, a major problem facing e-commerce platforms is how to quickly and accurately obtain a large number of products. From the information, the products that the user is interested in are filtered out and presented to the user. The personalized recommendation service is a powerful tool to deal with this problem. It can not only provide users with high-quality services, but also bring unprecedented profits to businesses.

       The user behavior in the e-commerce platform is meaningful, and it can even be said that every user's behavior operation reflects the essential needs of the user's heart. Therefore, this article analyzes the user behavior of Ali platform in terms of time evolution, behavior conversion, behavior time interval, and repurchase situation, and finds that the user churn rate after browsing is high and the purchase conversion rate is low, as well as some other user behavior characteristics. In order to meet the personalized needs of different users, improve the user's shopping experience, reduce user churn, increase purchase conversion rate, and create greater value for merchants, this article conducts research on personalized recommendations for e-commerce platforms.

   This article introduces three commonly used recommendation methods, compares and analyzes their advantages and disadvantages and their applicable scenarios. It is found that content-based recommendation methods are more suitable for text recommendation fields, and recommendation methods based on association rules are mainly used to discover shopping carts. The relevance between collaborative filtering methods is highly personalized, and the potential needs of users can be explored, and the interpretability is strong. For the e-commerce platform, in order to complete the user's personalized recommendation based on user behavior data, this paper finally chooses the user-based collaborative filtering recommendation method. But at the same time, it is also found that the collaborative filtering recommendation algorithm has data sparseness problems, cold start problems and scalability problems. In this regard, this article combines the collaborative filtering recommendation algorithm with the k-means clustering algorithm for product recommendation, and compares it with the traditional collaborative filtering recommendation algorithm. Experimental results show that for e-commerce platforms, the accuracy, recall, and F1 value of the collaborative filtering recommendation algorithm based on k-means clustering are better than those of the traditional collaborative filtering recommendation algorithm, and its computational complexity is also lower. While alleviating the problem of data sparsity, it also effectively solves the problem of scalability. It shows greater advantages in both recommendation performance and recommendation efficiency, which provides a certain reference for the personalized recommendation service of e-commerce platforms.