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中文摘要：
在线用户的群体兴趣对于分析在线社会网络以及个性化推荐至关重要。研究的目的是引入信息熵这一指标来准确度量用户兴趣的多样性。分别在电影网站MovieLens和音乐网站Last．FM数据上进行实证分析，即统计度相同的用户所选产品的信息熵值。MovieLens的结果表明，随着用户度的增加，熵值出现先上升后下降的趋势，即度小的用户和度大的用户的兴趣比较专一，而一般用户的兴趣较为多样；而Last．FM的结果表明，度小用户的兴趣非常多样，但随着用户听过的音乐数量越多，兴趣越明确。通过建立随机模型与实证结果进行比较，可以发现绝大多数用户在真实数据集上的兴趣的多样性比随机情况要大，可见用户的兴趣对用户行为模式的影响显著。
英文摘要：
Online users’collective interests played an important role for analyzing the online social networks and personalized re commendations.The purpose was to introduce the information entropy to measure the diversity of the user interests.This paper empirically calculated the information entropy of the objects selected by the users with the same degree in both the MovieLens and Last.FM data sets.The results for the MovieLens datasets show that the interests of the small-degree and large-degree users were more centralized,while the interests of normal users were more diverse.However,the results for the Last.FM indicated that was the user degree was the greater,the users’interests were the more centralized.In addition,it proposed a null model to be com-pared with the empirical results.The results show that the diversity of the majority of users in the real datasets is higher than that in random cases.As a result,users’interests have a significant impact on users’behavioral patterns.
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