(西华师范大学商学院 南充 637002)

针对数字图书馆协同过滤推荐系统所面临的用户评分数据稀疏性问题，提出群体兴趣偏向度的计算方法，对用户－项目评分矩阵空缺值进行预测。实验结果表明，该算法能有效提高推荐质量。

(Business College, China West Normal University, Nanchong 637002, China)

Collaborative filtering recommendation systems in digital library have faced the problem of sparse user ratings. To solve the problem, a computing method of group interest trend degree has been proposed and used into the prediction of vacant values in user-item matrix. The experimental results show that the algorithm can efficiently improve recommendation quality.

TP311

* 本文系西华师范大学科研启动基金项目“企业信息门户”(项目编号：04B022)的研究成果之一。

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