|
|
论文:2019,Vol:37,Issue(6):1294-1301 |
|
|
引用本文： |
|
|
白昀, 蔡皖东. 基于用户兴趣领域中可信圈挖掘的推荐模型[J]. 西北工业大学学报 |
|
|
BAI Yun, CAI Wandong. Recommendation Model for Trust Circle Mining Based on Users' Interest Fields[J]. Northwestern polytechnical university |
|
|
|
|
|
|
|
| 基于用户兴趣领域中可信圈挖掘的推荐模型 |
|
| 白昀, 蔡皖东 |
|
| 西北工业大学 计算机学院, 陕西 西安 710072 |
| 摘要: |
| 基于信任的推荐系统通过系统评分数据和用户信任关系为用户推荐所需资源。现有相关工作中在考虑信任关系时，通常考虑的是一种泛化的信任关系，尚未充分挖掘信任关系信息与特定兴趣领域之间的关系，对推荐的准确性和可靠性会产生一定的劣化影响。考虑到以上问题，提出基于用户兴趣领域的信任圈模型，针对不同兴趣领域分层挖掘用户间潜在的隐形信任关系；并充分融合显性信任关系为用户资源进行综合评分。该模型不仅考虑信任信息与领域的匹配关系，而且能够挖掘在具体领域下用户间的隐性信任关系，能够进一步提高评分预测的精确度和覆盖率。通过在Epinions数据集上的实验，证明了所提出的基于用户兴趣领域可信圈挖掘的推荐模型与基于泛化信任关系的传统推荐算法相比可以有效提高推荐评分预测的准确度和覆盖率。 |
| 关键词:
信任关系
兴趣领域
推荐算法
可信圈
社会网络
|
|
| Recommendation Model for Trust Circle Mining Based on Users' Interest Fields |
|
| BAI Yun, CAI Wandong |
|
| School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China |
| Abstract: |
| A trust-based recommendation system recommends the resources needed for users by system rating data and users' trust relationship. In current relevant work, an over-generalized trust relationship is likely to be considered without exploiting the relationship between trust information and interest fields, affecting the precision and reliability of the recommendation. This research, therefore, proposes a users' interest-field-based trust circle model. Based on different interest fields, it exploits potential implicit trust relationships in separated layers. Besides, it conducts user rating by combining explicit trust relationships. This model not only considers the matching between trust information and fields, but also explores the implicit trust relationships between users do not revealed in specific fields, thus it is able to improve the precision and coverage of rating prediction. The experiments made with the Epinions data set proved that the recommendation model based on trust circle exploiting in users' interest fields proposed in this research, is able to effectively improve the precision and coverage of the recommendation rating prediction, compared with the traditional recommendation algorithm based on generalized trust relationship. |
| Key words:
trust relationship
interest field
recommendation algorithm
trust circle
social network
|
|
| 收稿日期: 2019-02-27
修回日期:
|
| DOI: 10.1051/jnwpu/20193761294 |
| 基金项目: 榆林市科技计划项目（2016-24-4）与陕西省教育厅专项科学研究计划项目（19JK0526）资助 |
| 通讯作者:
Email： |
| 作者简介: 白昀(1985-),女,西北工业大学博士研究生,主要从事社交网络的信任模型、概率图研究。
|
|
| 相关功能 |
|
|
|
|
| 作者相关文章 |
|
| 白昀 在本刊中的所有文章 |
| 蔡皖东 在本刊中的所有文章 |
|
|
|
|
|
|
|
|
参考文献: |
|
|
[1] SU X, KHOSHGOFTAAR T M. A Survey of Collaborative Filtering Techniques[J]. Advances in Artificial Intelligence, 2009,12(1):1-19
[2] YANG X, GUO Y, LIU Y, et al. Bayesian-Inference-Based Recommendation in Online Social Networks[J]. IEEE Trans on Parallel and Distributed Systems, 2013, 24(4):642-651
[3] LIU F, LEE H J. Use of Social Network Information to Enhance Collaborative Filtering Performance[J]. Expert Systems with Applications, 2010, 37(7):4772-4778
[4] DELIC A, MASTHOFF J, NEIDHARDT J, et al. How to Use Social Relationships in Group Recommenders:Empirical Evidence[C]//Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, 2018:121-129
[5] TAHERI S M, MAHYAR H, FIROUZI M, et al. Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction[C]//Proceedings of the 26th International Conference on World Wide Web Companion, 2017:1343-1351
[6] JAMALI M, ESTER M. Trustwalker:a Random Walk Model for Combining Trust-Based and Item-Based Recommendation[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009
[7] MASSA P, AVESANI P. Trust-Aware Recommender Systems[C]//Proceedings of the 2007 ACM Conference on Recommender Systems, 2007:17-24
[8] ZHANG B, HUANG Z, YU J, et al. Trust Computation for Multiple Routes Recommendation in Social Network Sites[J]. Security and Communication Networks, 2014, 7(12):2258-2276
[9] MA H, KING I, LYU M R, et al. Learning to Recommend with Explicit and Implicit Social Relations[J]. ACM Trans on Intelligent Systems and Technology, 2011, 2(3):1-19
[10] MA H. An Experimental Study on Implicit Social Recommendation[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2013:73-82
[11] MCAULEY J, LESKOVEC J. Discovering Social Circles in Ego Networks[J]. ACM Trans on Knowledge Discovery from Data, 2014, 8(1):1-28
[12] BURTON S H, GIRAUDCARRIER C G. Discovering Social Circles in Directed Graphs[J]. ACM Trans on Knowledge Discovery from Data, 2014, 8(4):1-27
[13] ZHONG T, LIU F, ZHOU F, et al. Motion Based Inference of Social Circles via Self-Attention and Contextualized Embedding[J]. IEEE Access, 2019, 7:61934-61948
[14] LAN C, YANG Y, LI X, et al. Learning Social Circles in Ego-Networks Based on Multi-View Network Structure[J]. IEEE Trans on Knowledge and Data Engineering, 2017, 29(8):1681-1694
[15] YANG X, STECK H, LIU Y. Circle-Based Recommendation in Online Social Networks[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012
[16] YIN B, YANG Y, LIU W. ICSRec:Interest Circle-Based Recommendation System Incorporating Social Propagation[C]//4th IEEE International Conference on Information Science and Technology, 2014:250-255 |
|
|
|
|
|
|
|
