Personalized Service Recommendation Method Based on Random Walk and Diversified Graph Ranking

FANG Chen; ZHANG Heng-wei; WANG Na; WANG Jin-dong

doi:10.3969/j.issn.0372-2112.2018.11.027

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Personalized Service Recommendation Method Based on Random Walk and Diversified Graph Ranking

更新时间：2025-07-16

- Personalized Service Recommendation Method Based on Random Walk and Diversified Graph Ranking
- Acta Electronica Sinica Vol. 46, Issue 11, Pages: 2773-2780(2018)
- 作者机构：
  
  1. 信息工程大学,河南,郑州,450001
  2. 数字工程与先进计算国家重点实验室,河南,郑州,450001
  3. 信息工程大学,河南,郑州,450001
  4. 数字工程与先进计算国家重点实验室,河南,郑州,450001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61309013, No.61303074);Technology Research and Development Program Fund of Henan Province (No.12210231003)
- DOI：10.3969/j.issn.0372-2112.2018.11.027
  CLC： TP393
- Published Online：25 November 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
FANG Chen, ZHANG Heng-wei, WANG Na, et al. Personalized Service Recommendation Method Based on Random Walk and Diversified Graph Ranking[J]. Acta Electronica Sinica, 2018, 46(11): 2773-2780.
DOI：

FANG Chen, ZHANG Heng-wei, WANG Na, et al. Personalized Service Recommendation Method Based on Random Walk and Diversified Graph Ranking[J]. Acta Electronica Sinica, 2018, 46(11): 2773-2780. DOI： 10.3969/j.issn.0372-2112.2018.11.027.

摘要

针对传统服务推荐算法由于数据稀疏性而导致推荐准确性不高，以及推荐结果缺乏多样性等缺陷，提出基于随机游走和多样性图排序的个性化服务推荐方法（PRWDR）.在分析直接相似关系稀疏性的基础上提出带权重的随机游走模型，通过在用户网络上进行随机游走来挖掘更多的相似关系；基于所有相似用户预测服务的QoS值，并给出服务图模型构建方法，以过滤大量性能过低的候选服务；提出最优节点集合选取策略，利用贪婪算法得到兼具推荐准确性和功能多样性的服务推荐列表.在公开发布的数据集上进行实验，并与多个经典算法进行比较，验证了本算法的有效性.

Abstract

In view of the low recommendation accuracy due to the sparseness of data

and the lack of diversity in traditional service recommendation algorithms

personalized service recommendation method based on random walking and diversified graph ranking (PRWDR) is proposed. On the basis of analyzing the sparseness of direct similarity relationships

a weighted random walk model is proposed

which can excavate more similarity relationships by random walk on the user network. The QoS value of services is predicted based on all similar users

and then the service graph model construction method is presented to filter those services with low performance. By using the greedy algorithm

the optimal node collection selection strategy is proposed to obtain the service recommendation list with both accuracy and diversity. By testing the algorithm on the public dataset and also comparing with several classic algorithms

the validity of PRWDR is verified.

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