New interest-sensitive and network-sensitive method for user recommendation

Yan-min SHANG; Peng ZHANG; Ya-nan CAO

doi:10.11959/j.issn.1000-436x.2015040

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New interest-sensitive and network-sensitive method for user recommendation

Academic papers | 更新时间：2024-06-05

- New interest-sensitive and network-sensitive method for user recommendation
- Journal on Communications Vol. 36, Issue 2, Pages: 117-125(2015)
- 作者机构：
  
  1. 中国科学院计算技术研究所，北京 100190
  2. 中国科学院信息工程研究所，北京 100093
- 作者简介：
- 基金信息：
  
  The National High Technology Research and Development Program of China (863 Program)(2011AA010705);The Knowledge Innovation Pro-gram of the Chinese Academy of Sciences(XDA06030200);Priority Research Program (XDA06030200); The National Natural Science Foundation of China(61003167)
- DOI：10.11959/j.issn.1000-436x.2015040
  CLC： TP391
- Online First：2015-02，
  
  Published：25 February 2015
- 稿件说明：
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Yan-min SHANG, Peng ZHANG, Ya-nan CAO. New interest-sensitive and network-sensitive method for user recommendation[J]. Journal on Communications, 2015, 36(2): 117-125.
DOI：

Yan-min SHANG, Peng ZHANG, Ya-nan CAO. New interest-sensitive and network-sensitive method for user recommendation[J]. Journal on Communications, 2015, 36(2): 117-125. DOI： 10.11959/j.issn.1000-436x.2015040.

摘要

提出一种新的朋友推荐方法，该方法同时使用用户兴趣和朋友关系这2种因素来为目标用户推荐朋友，对PageRank算法进行改进，提出一种能同时融合上述2种因素的Topic_Friend_PageRank(TFPR)模型。首先，采用LDA(latent Dirichlet allocation)分析用户发布的消息内容，将用户表示为若干主题上的分布，从而建模用户的兴趣。接下来，使用加权的 PageRank 算法建模用户在整个链接拓扑中的重要程度和用户之间朋友关系的相似性。最后根据主题感知的PageRank思想，将用户兴趣融入前面提到的加权PageRank中，形成同时融合用户兴趣和朋友关系的TFPR模型。采用新浪微博数据验证所提模型的性能，实验证明该模型能同时得到较高的准确率和召回率。

Abstract

A new hybrid approach by incorporatin gusers’ interests and users’ friendships together to recommend new friends for target users is proposed.A variation of PageRank—Topic_Friend_PageRank(TFPR) is proposed

which can consider user interests and user friends at same time.Firstly

proposed method uses latent Dirichlet allocation (LDA) to model users’ interests

and weighted-PageRank algorithm to model users’ friendship network

and then merge these two factors into TFPR.This hybrid method models users’ interests and users’ friendships at the same time

and wedemonstrate the effectiveness of proposed hybrid model by using some social network datasets.

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Keywords

references

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