基于评级的推荐系统的随机森林方法

A. Ajesh, Jayashree Nair, P. S. Jijin
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引用次数: 20

摘要

推荐系统在促进销售的同时,也成为了网络购物网站不可缺少的一部分。根据用户喜好推荐直观的产品,解决了信息过载的问题。推荐系统构成信息过滤机制,对海量数据进行过滤。像SVD, KNN, Softmax Regression这样的算法在过去已经被用来形成推荐。在本文中,我们提出了一个系统,该系统使用聚类和随机森林作为多层策略来预测基于用户评分的推荐,同时针对用户的心态和当前趋势。使用均方根误差(RMSE)对结果进行了评估。取得了可行的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A random forest approach for rating-based recommender system
Recommender system has emerged as an integral part of the online shopping sites as it promotes sales. It recommends intuitive products based on users preference which solves the issue of information overload. Recommender system constitutes information filtering mechanism which filters vast amount of data. Algorithms such as SVD, KNN, Softmax Regression has already been used in the past to form recommendations. In this paper we propose a system which uses clustering and random forest as multilevel strategies to predict recommendations based on users ratings while targeting users mind-set and current trends. The result has been evaluated with the help of RMSE (Root Mean Square Error). Feasible performance has been achieved.
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