摘 要：PersonalRank 就是一种基于随机游走的图推荐算法。传统的 PersonalRank 算法是在 pagePank 算法的基础上进行了改进，但依旧存在个性化推荐召回率和准确率不高，过度偏重同物品中其他用户操作的问题，导致覆盖率不高。现针对PersonalRank 问题，文章从推荐系统评测指标的覆盖率、召回率和准确率三个维度出发，加强算法发掘长尾的能力，同时提高推荐结果的用户满意度。
关键词：信息资源；图推荐算法；PersonalRank；长尾
DOI:10.19850/j.cnki.2096-4706.2021.15.007
中图分类号：TP391 文献标识码：A 文章编号：2096-4706（2021）15-0025-04
Improved Personalrank Algorithm for Personalized Recommendation
LI Wei
(Renmin University of China, Beijing 100872, China)
Abstract: PersonalRank is a graph recommendation algorithm based on random walk. The traditional PersonalRank algorithm is based on the changes made by pagepank algorithm, but there are still problems of low recall and accuracy of personalized recommendation and excessive emphasis on the operation of other users in the same item, resulting in low coverage. Aiming at the PersonalRank problems, the paper starts from three dimensions of the evaluation indicators of the recommendation system: coverage, recall and accuracy, strengthens the ability of algorithms to discover long tail and improves the user satisfaction of the recommendation results.
Keywords: information resources; graph recommendation algorithm; PersonalRank; long tail
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作者简介：李维（1993.05—），男，汉族，湖北荆州人，开发工程师，本科，研究方向：个性化推荐系统。