一种基于线性回归的新型推荐方法

A recommendation algorithm based on linear regression method

随着社交媒体的大力发展,互联网不再只是人们获取信息的工具,同时还是人们分享信息的渠道。用户生成内容使得人们面临着信息过载,大量真正有价值的信息难以被发现。个性化推荐系统凭借其较低的用户参与度被认为是当前解决信息过载最有潜力的方法之一。然而,目前最成熟、应用最广的协同过滤推荐方法正面临着数据稀疏性、多样性等问题,其推荐效果不甚理想。本文提出了一种基于线性回归的推荐方法,利用用户或物品的评分频次信息,建立了线性回归模型,以此预测用户对未评分物品的评分。该方法具有低复杂性、可增量更新、高准确性等优点。

With the development of social media, Internet is not only people＇s tool to get information, but also a channel to share information. User-generated contents make people face overload information. So that a lot of really valuable information is difficult to be found. On the strength of lower user involvement, the personalized recommendation system has been considered as one of the most potential methods to solve information overload at present. However, currently the most mature and widely used collaborative filtering recommendation method is facing such problems as data sparseness, diversity and so on. Its recommended effect is not ideal. A recommendation method based on linear regression is proposed in this paper. A linear regression model is established by using the rating frequency information of the users or items to predict the uses＂scores on non-scored items. The method has the advantages of low complexity, incremental updating, and high accuracy and so on.