计算机科学 ›› 2018, Vol. 45 ›› Issue (10): 37-42.doi: 10.11896／j.issn.1002-137X.2018.10.007
• 2018 年中国粒计算与知识发现学术会议 • 上一篇 下一篇
张紫茵, 张恒汝, 徐媛媛, 秦琴
ZHANG Zi-yin, ZHANG Heng-ru, XU Yuan-yuan, QIN Qin
摘要： 数据稀疏性是目前协同过滤面临的主要挑战之一。用户间的信任关系为推荐系统提供了有用的附加信息。已有工作主要采用直接信任关系作为附加信息,对间接信任关系考虑得较少。针对这一情况,提出一种融合直接和间接的用户非对称信任关系的推荐算法(ATRec)。首先,构建一种信任传递机制,并利用该机制获得用户间的间接非对称信任关系。其次,根据直接和间接非对称信任关系获得每个用户的信任集合。最后,利用信任集合、最近邻的评分和好评阈值计算出商品的受欢迎程度,进而获得对用户的top-N推荐列表。在真实数据集上的实验结果表明,该算法比主流的推荐算法在top-N推荐性能上有更好的表现。
中图分类号:
| [1]MASSA P,AVESANI P.Trust-aware recommender systems [C]∥2007 ACM Conference on Recommender Systems.ACM,2007:17-24. [2]ZHAO W X,LI S,HE Y,et al.Connecting social media to e-commerce:Cold-start product recommendation using microblogging information[J].IEEE Transactions on Knowledge and Data Engineering,2016,28(5):1147-1159. [3]ZHOU J,TANG M,TIAN Y,et al.Social network and tag sources based augmenting collaborative recommender system[J].IEICE Transactions on Information and Systems,2015,98(4):902-910. [4]ZHANG H R,MIN F.Three-way recommender systems based on random forests[J].Knowledge-Based Systems,2016,91(C):275-286. [5]O’DONOVAN J,SMYTH B.Trust in r-commender systems [C]∥10th International Conference on Intelligent User Interfaces.ACM,2005:167-174. [6]DENG A L,ZHU Y Y,SHI B L.A collaborative filtering re- commendation algorithm based on item rating prediction[J].Journal of Software,2003,14(9):1621-1628.(in Chinese) 邓爱林,朱扬勇,施伯乐.基于商品评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628. [7]SINHA R R,SWEARINGEN K.Comparing recommendations made by online systems and friends [C]∥DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries.2001. [8]MASSA P,BHATTACHARJEE B.Using trust in recommender systems:an experimental analysis[C]∥International Conference on Trust Management.Berlin:Springer,2004:221-235. [9]MA H,ZHOU D,LIU C,et al.Recommender systems with social regularization[C]∥Forth International Conference on Web Search & Web Data Mining.DBLP,2011:287-296. [10]HERLOCKER J L,KONSTAN J A,RIEDL J.Explaining collaborative filtering recommendations[C]∥2000 ACMConfe-rence on Computer Supported Cooperative Work.ACM,2000:241-250. [11]PARK C,KIM D,OH J,et al.Improving top-K recommendation with truster and trustee relationship in user trust network[J].Information Sciences An International Journal,2016,374(C):100-114. [12]PAN R,ZHOU Y,CAO B,et al.One-class collaborative filtering[C]∥Eighth IEEE International Conference on Data Mining,2008(ICDM’08).IEEE,2008:502-511. [13]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]∥Twenty-fifth Conference on Uncertainty in Artificial Intelligence.AUAI Press,2009:452-461. [14]SHI Y,LARSON M,HANJALIC A.List-wise learning to rank with matrix factorization for collaborative filtering[C]∥Fourth ACM Conference on Recommender Systems.ACM,2010:269-272. [15]ZHAO T,MCAULEY J,KING I.Leveraging social connections to improve personalized ranking for collaborative filtering[C]∥23rd ACM International Conference on Information and Know-ledge Management.ACM,2014:261-270. [16]YAO W,HE J,HUANG G,et al.SoRank:incorporating social information into learning to rank models for recommendation[C]∥23rd International Conference on World Wide Web.ACM,2014:409-410. |
| [1] | 程章桃, 钟婷, 张晟铭, 周帆. 基于图学习的推荐系统研究综述 Survey of Recommender Systems Based on Graph Learning 计算机科学, 2022, 49(9): 1-13. https://doi.org/10.11896/jsjkx.210900072 |
| [2] | 王冠宇, 钟婷, 冯宇, 周帆. 基于矢量量化编码的协同过滤推荐方法 Collaborative Filtering Recommendation Method Based on Vector Quantization Coding 计算机科学, 2022, 49(9): 48-54. https://doi.org/10.11896/jsjkx.210700109 |
| [3] | 秦琪琦, 张月琴, 王润泽, 张泽华. 基于知识图谱的层次粒化推荐方法 Hierarchical Granulation Recommendation Method Based on Knowledge Graph 计算机科学, 2022, 49(8): 64-69. https://doi.org/10.11896/jsjkx.210600111 |
| [4] | 方义秋, 张震坤, 葛君伟. 基于自注意力机制和迁移学习的跨领域推荐算法 Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning 计算机科学, 2022, 49(8): 70-77. https://doi.org/10.11896/jsjkx.210600011 |
| [5] | 帅剑波, 王金策, 黄飞虎, 彭舰. 基于神经架构搜索的点击率预测模型 Click-Through Rate Prediction Model Based on Neural Architecture Search 计算机科学, 2022, 49(7): 10-17. https://doi.org/10.11896/jsjkx.210600009 |
| [6] | 齐秀秀, 王佳昊, 李文雄, 周帆. 基于概率元学习的矩阵补全预测融合算法 Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning 计算机科学, 2022, 49(7): 18-24. https://doi.org/10.11896/jsjkx.210600126 |
| [7] | 蔡晓娟, 谭文安. 一种改进的融合相似度和信任度的协同过滤算法 Improved Collaborative Filtering Algorithm Combining Similarity and Trust 计算机科学, 2022, 49(6A): 238-241. https://doi.org/10.11896/jsjkx.210400088 |
| [8] | 何亦琛, 毛宜军, 谢贤芬, 古万荣. 基于点割集图分割的矩阵变换与分解的推荐算法 Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation 计算机科学, 2022, 49(6A): 272-279. https://doi.org/10.11896/jsjkx.210600159 |
| [9] | 洪志理, 赖俊, 曹雷, 陈希亮, 徐志雄. 基于遗憾探索的竞争网络强化学习智能推荐方法研究 Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration 计算机科学, 2022, 49(6): 149-157. https://doi.org/10.11896/jsjkx.210600226 |
| [10] | 郭亮, 杨兴耀, 于炯, 韩晨, 黄仲浩. 基于注意力机制和门控网络相结合的混合推荐系统 Hybrid Recommender System Based on Attention Mechanisms and Gating Network 计算机科学, 2022, 49(6): 158-164. https://doi.org/10.11896/jsjkx.210500013 |
| [11] | 熊中敏, 舒贵文, 郭怀宇. 融合用户偏好的图神经网络推荐模型 Graph Neural Network Recommendation Model Integrating User Preferences 计算机科学, 2022, 49(6): 165-171. https://doi.org/10.11896/jsjkx.210400276 |
| [12] | 余皑欣, 冯秀芳, 孙静宇. 结合物品相似性的社交信任推荐算法 Social Trust Recommendation Algorithm Combining Item Similarity 计算机科学, 2022, 49(5): 144-151. https://doi.org/10.11896/jsjkx.210300217 |
| [13] | 陈壮, 邹海涛, 郑尚, 于化龙, 高尚. 基于用户覆盖及评分差异的多样性推荐算法 Diversity Recommendation Algorithm Based on User Coverage and Rating Differences 计算机科学, 2022, 49(5): 159-164. https://doi.org/10.11896/jsjkx.210300263 |
| [14] | 陈晋鹏, 胡哈蕾, 张帆, 曹源, 孙鹏飞. 融合时间特性和用户偏好的卷积序列化推荐 Convolutional Sequential Recommendation with Temporal Feature and User Preference 计算机科学, 2022, 49(1): 115-120. https://doi.org/10.11896/jsjkx.201200192 |
| [15] | 詹皖江, 洪植林, 方路平, 吴哲夫, 吕跃华. 基于对抗性学习的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning 计算机科学, 2021, 48(7): 172-177. https://doi.org/10.11896/jsjkx.200600077 |
|
||
首页