"基于SLIM算法的高质量个性化推荐系统研究"

随着互联网的发展，网络商业突飞猛进，种类越来越丰富。由互联网带起的商业模式不断在改变着我们的生活。然而，这些基于网络的营销模式也缺乏一些实体店有的但是网络应用很难有的服务：个性化推荐。当用户开始浏览某个现代商业网站或打开某个商业软件，出现的是永远看不完的商品。需求明确的用户可以通过搜索引擎找到自己需要的产品，而对于那些需求不明确的用户甚至只是想随便逛一逛看一看的用户，给这些用户进行如同实体店里面那样的个性化推荐可以增加潜在的产品销量。既然不能人工进行个性化推荐，我们自然就想到利用大数据分析自动给用户进行个性化推荐。推荐系统就这样诞生了。一个高质量的推荐算法可以十分有效的提升这些网络商业应用的利润。我的论文就是来研究一种高质量的推荐算法：SLIM推荐算法。我在本论文对这种推荐算法进行详细的过程分析，测试并评估推荐结果。我的实验表明，SLIM推荐算法是一种质量高，实用性强的推荐算法。 关键词：推荐系统，SLIM推荐算法。 Abstract With the development of the internet, online business has been flourishing, and the variety of goods and services has been increasing. The business models brought about by the internet continue to change our lives. However, these online marketing models lack some services that physical stores have but are difficult to implement in web applications, such as personalized recommendations. When users start browsing a modern commercial website or open a commercial software, they are presented with an endless list of products. For users with clear needs, they can use search engines to find the products they are looking for. However, for those users who have unclear needs or are just browsing, providing personalized recommendations similar to those in physical stores can increase potential product sales. Since manual personalized recommendations are not feasible, the use of big data analysis for automatic personalized recommendations for users has become a natural choice. Thus, the recommendation system was born. A high-quality recommendation algorithm can significantly improve the profits of these online commercial applications. My thesis is to research a high-quality recommendation algorithm: the SLIM recommendation algorithm. In this paper, I provide a detailed analysis of this recommendation algorithm, test its performance, and evaluate the recommendation results. My experiments show that the SLIM recommendation algorithm is a high-quality and practical recommendation algorithm. Keywords: recommendation system, SLIM recommendation algorithm.