基于商品属性与用户聚类的个性化服装推荐研究
发布时间：2019-01-11 12:49
【摘要】：淘宝网作为电子商务时代最大的网上零售平台,为用户提供越来越多的商品与服务的同时,也出现了信息过载等一系列问题。鉴于此,本文提出了基于商品属性与用户聚类的个性化服装推荐方法,通过用户个人信息与对商品的评价,计算用户之间的相似度,进行聚类分析。与此同时,将商品化整为零,通过商品属性来计算商品的相似度,得到top-N相似列表。以此,综合商品与用户两者的权重值,实现为用户提供个性化的商品推荐,解决用户面对信息过载的难题,为用户节省精力,提高用户的购物体验。针对某一淘宝网店铺,本文提出了适合的混合推荐算法,并通过搜集实际数据进行了实证研究,对推荐结果进行准确性评价。
[Abstract]:Taobao, as the largest online retail platform in the era of electronic commerce, provides more and more goods and services to users, at the same time, there are a series of problems such as information overload and so on. In view of this, this paper proposes a personalized clothing recommendation method based on commodity attributes and user clustering, which calculates the similarity between users and makes clustering analysis through the personal information of users and the evaluation of commodities. At the same time, the commodity is divided into zero, and the similarity of goods is calculated by commodity attributes, and the top-N similarity list is obtained. In this way, the weights of both commodities and users can be synthesized to provide personalized commodity recommendation for users, solve the problem that users face information overload, save energy for users and improve their shopping experience. For a Taobao shop, this paper proposes a suitable hybrid recommendation algorithm, and through the collection of practical data for empirical research, the accuracy of the recommendation results are evaluated.
【作者单位】： 武汉大学信息管理学院;
【分类号】：F724.6;F426.86
[Abstract]:Taobao, as the largest online retail platform in the era of electronic commerce, provides more and more goods and services to users, at the same time, there are a series of problems such as information overload and so on. In view of this, this paper proposes a personalized clothing recommendation method based on commodity attributes and user clustering, which calculates the similarity between users and makes clustering analysis through the personal information of users and the evaluation of commodities. At the same time, the commodity is divided into zero, and the similarity of goods is calculated by commodity attributes, and the top-N similarity list is obtained. In this way, the weights of both commodities and users can be synthesized to provide personalized commodity recommendation for users, solve the problem that users face information overload, save energy for users and improve their shopping experience. For a Taobao shop, this paper proposes a suitable hybrid recommendation algorithm, and through the collection of practical data for empirical research, the accuracy of the recommendation results are evaluated.
【作者单位】： 武汉大学信息管理学院;
【分类号】：F724.6;F426.86
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