基于万有引力和随机行走的推荐算法研究

Study on GARW recommendation algorithm

就社会标签系统中的个性化推荐算法进行了研究,提出一种基于万有引力和随机游走的个性化推荐算法。针对现有推荐算法缺乏物理学解释和单纯依靠用户评分等问题,该算法创新性地将万有引力原理引入推荐系统,定义了项目的万有引力及其计算方法,并以项目间万有引力大小来衡量项目间的相似度,从而得到项目相关图;然后,令用户兴趣点在项目相关图上随机游走,计算它在图上各节点的稳定概率,并以此作为用户和各节点亲密程度的度量值,该值高者就可能是用户喜欢的项目,从而推荐给用户。实验结果说明此算法较其他的相关推荐算法可以获得更高的推荐性能。

This paper studied the personalized recommendation algorithm for social tags systems, and proposed a new recom- mendation algorithm based on gravitation and random walk（GARW）. Aiming at the problem that the present recommendation algorithm lacked of physical explanation and heavy reliance on users＇ score, this new algorithm introduced into the universal law of gravitation innovatively, also gave the definition of item gravitation and the calculation method, and considered the strength of item gravitation as the similarity of items, then got the correlation graph of items. After that, using random walk method, it spreaded users＇ preferences on the correlation graph of items, got its＇ steady-state probability on nodes of the graph which reflec- ted the degree of correlation between users＇ preference and items. The items with higher steady-state probability were recommen- ded to the user. Experimental results show the new algorithm performs better than the other algorithm compared to.