一种基于有偏随机漫步的个性化推荐算法

2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE) Pub Date : 2014-05-14 DOI:10.1109/JCSSE.2014.6841883

Dawei Nie, Yan Fu, Junlin Zhou, Zhen Liu, Zi-Ke Zhang, Chuang Liu

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引用次数: 11

摘要

随着互联网的快速发展，推荐系统可以帮助我们在信息时代高效地找到有用的对象。传统的随机漫步算法精度较高，但个性和多样性较低。在本文中，我们提出了一种改进的随机漫步算法，通过抑制大程度目标的影响。在MovieLens和Netflix数据集上的实验结果表明，该算法不仅可以有效提高准确率(分别提高5.5%和5.9%)，而且可以有效提高多样性。

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A personalized recommendation algorithm via biased random walk

With the rapid development of Internet, Recommender Systems can help us efficiently find the useful objects in the information era. Generally, the traditional random walk algorithm has high accuracy but low personality and diversity. In this paper, we propose an improved random walk algorithm by depressing the influence of large-degree objects. Experimental results on MovieLens and Netflix data sets show that this algorithm can effectively improve not only the accuracy (improved by 5.5% and 5.9%, respectively) but also the diversity.

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来源期刊

2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE)

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