《计算机应用研究》|Application Research of Computers

填补法和改进相似度相结合的协同过滤算法

Collaborative filtering algorithm combining filling and improving similarity

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作者 邢长征,金媛
机构 辽宁工程技术大学 电子与信息工程学院,辽宁 葫芦岛 125105
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文章编号 1001-3695(2019)06-009-1643-03
DOI 10.19734/j.issn.1001-3695.2017.12.0813
摘要 针对稀疏的用户评分数据,国内外学者对协同过滤算法作了很多改进,归纳为填充法、改进相似度方法、结合内容的推荐等,这些单一方法都不能真正解决数据稀疏的问题。针对这个问题,提出一种填充法和改进相似度相结合的协同过滤算法。该算法首先利用填充法随机填充部分数据,改进的填充法预测评分时融入了项目属性信息,然后利用填充后的数据和新相似度方法作推荐,产生推荐结果,迭代<i>m</i>次,按照迭代<i>m</i>次被推荐项目平均评分的高低进行最后的推荐。实验表明,在数据稀疏的情况下,该算法与单一的方法比有更好的推荐效果。
关键词 协同过滤算法; 填补法; 新相似度方法; 结果融合
基金项目
本文URL http://www.arocmag.com/article/01-2019-06-009.html
英文标题 Collaborative filtering algorithm combining filling and improving similarity
作者英文名 Xing Changzheng, Jin Yuan
机构英文名 School of Electronic Information Engineering,Liaoning Technical University,Huludao Liaoning 125105,China
英文摘要 Aiming at the sparse user rating data, domestic and foreign scholars have made many improvements on collaborative filtering algorithm, which were summarized as filling user rating data, improving similarity, fusing content to recommend and so on. These single methods can't solve the problem of data sparseness. In order to solve this problem, this paper proposed a collaborative filtering algorithm which combines the filling data and improving similarity. Firstly, it used the improved filling method which increased the item's attribute information to fill the user rating data, and then recommended using new similarity method, produced the recommended results, iterated <i>m</i> times. Finally it recommended items according to the average score of scores got in <i>m</i> iterations. The experiment shows that the proposed algorithm has a better recommendation effect than single methods in the case of sparse user rating data.
英文关键词 collaborative filtering algorithm; filling method; new similarity method; result fusion
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收稿日期 2017/12/21
修回日期 2018/2/5
页码 1643-1645,1651
中图分类号 TP301.6
文献标志码 A