LBSN中融合信任与不信任关系的兴趣点推荐

朱敬华; 明骞

doi:10.11959/j.issn.1000-436x.2018117

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LBSN中融合信任与不信任关系的兴趣点推荐

学术论文 | 更新时间：2024-06-05

- LBSN中融合信任与不信任关系的兴趣点推荐
- POI recommendation by incorporating trust-distrust relationship in LBSN
- 通信学报 2018年39卷第7期页码：157-165
- 作者机构：
  
  黑龙江大学计算机科学与技术学院，黑龙江哈尔滨 150080
- 作者简介：
  
  [ "朱敬华（1976-），女，博士，黑龙江大学教授、硕士生导师，主要研究方向为社会网络推荐、传感器网络、数据挖掘。" ]
  [ "明骞（1991-），男，黑龙江大学硕士生，主要研究方向为基于位置社交网络的个性化推荐。" ]
- 基金信息：
  
  国家自然科学基金青年基金资助项目(61100048);国家自然科学基金资助项目(61370222);黑龙江省自然科学基金资助项目(F2016034)
- DOI：10.11959/j.issn.1000-436x.2018117
  中图分类号： TP311
- 网络出版日期：2018-07，
  
  纸质出版日期：2018-07-25
- 稿件说明：
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朱敬华, 明骞. LBSN中融合信任与不信任关系的兴趣点推荐[J]. 通信学报, 2018,39(7):157-165.

Jinghua ZHU, Qian MING. POI recommendation by incorporating trust-distrust relationship in LBSN[J]. Journal on communications, 2018, 39(7): 157-165.
朱敬华, 明骞. LBSN中融合信任与不信任关系的兴趣点推荐[J]. 通信学报, 2018,39(7):157-165. DOI： 10.11959/j.issn.1000-436x.2018117.

Jinghua ZHU, Qian MING. POI recommendation by incorporating trust-distrust relationship in LBSN[J]. Journal on communications, 2018, 39(7): 157-165. DOI： 10.11959/j.issn.1000-436x.2018117.

摘要

兴趣点（POI

point of interest）推荐是位置社交网络（LBSN

location-based social network）重要的个性化服务，广泛用于热门景点推荐和旅游线路规划等。传统的基于协同过滤的推荐算法根据用户相似性和位置相似性进行推荐，未考虑推荐用户与目标用户间的信任关系，而信任关系有助于提高推荐系统的准确性、顽健性和用户满意度。首先分析了信任与不信任关系的传播特征，然后给出了信任度的表示和计算方法，最后提出了融合用户相似性、地理位置相似性和信任关系的混合推荐模型。实验结果表明，与传统协同过滤推荐方法相比，融合信任关系的混合推荐方法显著提高了推荐结果的准确性和用户满意度。

Abstract

POI (point of interest) recommendation is an important personalized service in the LBSN (location-based social network) which has wide applications such as popular sights recommendation and travel routes planning.Most existing collaborative filter algorithms make recommendation according to user similarity and location similarity

they don’t consider the trust relationship between users.And trust relationship is helpful to improve recommendation accuracy

robustness and user satisfaction.Firstly

the propagation property of trust and distrust relationship was analyzed.Then

the measurement and computation method of trust were given.Finally

a hybrid recommendation system which combined user similarity

geographical location similarity and trust relationship was proposed.The experiments results show that the hybrid recommendation is obviously superior to the traditional collaborative filtering in terms of results accuracy and user satisfaction.

关键词

Keywords

references

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