融合Jensen-Shannon散度的推荐算法

doi:10.11896/j.issn.1002-137X.2019.02.032

计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 210-214.doi: 10.11896/j.issn.1002-137X.2019.02.032

融合Jensen-Shannon散度的推荐算法

王永¹, 王永东¹, 邓江洲¹, 张璞²

重庆邮电大学经济管理学院重庆400065¹
重庆邮电大学计算机科学与技术学院重庆400065²

收稿日期:2017-12-06 出版日期:2019-02-25 发布日期:2019-02-25
通讯作者: 王永(1977-),男,博士,教授,CCF会员,主要研究方向为数据挖掘、信息系统和加密算法等,E-mail:wangyong1@cqupt.edu.cn
作者简介:王永东(1994-),男,硕士生,主要研究方向为推荐算法;邓江洲(1993-),男,硕士生,主要研究方向为数据挖掘和文本处理;张璞(1976-),男,博士,副教授,主要研究方向为自然语言和数据挖掘等。
基金资助:
本文受国家社会科学基金项目(15XGL024),重庆市前沿与应用基础研究计划项目(cstc2015jcyjA40025)资助。

Recommendation Algorithm Based on Jensen-Shannon Divergence

WANG Yong¹, WANG Yong-dong¹, DENG Jiang-zhou¹, ZHANG Pu²

School of Economics and Managements,Chongqing University of Posts and Telecommunications,Chongqing 400065,China¹
School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China²

Received:2017-12-06 Online:2019-02-25 Published:2019-02-25

摘要/Abstract

摘要： 为充分利用所有评分,缓解数据稀疏性问题,将概率统计领域的Jensen-Shannon(JS)散度引入相似性度量中,提出了一种新的项目相似性度量算法。该算法将项目的评分信息转化为评分值密度,并依据评分值的密度分布来计算项目相似性。同时,引入评分数量因子,进一步提升了基于JS的相似性度量方法的性能。最后,以基于JS的相似性度量方法为基础,设计了相应的协同过滤算法。在MovieLens数据集上的实验结果表明,所提算法在预测误差和推荐准确性方面均有良好的表现。因此,该算法在推荐系统中具有很好的应用潜力。

关键词: Jensen-Shannon散度, 评分值密度, 数据稀疏性, 相似性度量, 协同过滤

Abstract: To fully utilize all the ratings and weaken the problem of data sparsity,the Jensen-Shannon divergence in statistics field was used to design a new similarity measure for items.In this similarity measure,the ratings for items are converted to the density of rating values.Then,the item similarity is calculated according to the density of rating values.Meanwhile,the factor for the number of ratings is also considered to further enhance the performance of the proposed similarity measure based on JS divergence.Finally,a collaborative filtering recommendation algorithm is presented according to the JS-divergence-based item similarity.The test results on MovieLens dataset show that the proposed algorithm has good performance in prediction error and recommendation precision.Therefore,it has high potential to be applied in recommendation system.

Key words: Collaborative filtering, Data sparsity, Density of ratings, Jensen-Shannon divergence, Similarity measure

中图分类号:

TP391

王永, 王永东, 邓江洲, 张璞. 融合Jensen-Shannon散度的推荐算法[J]. 计算机科学, 2019, 46(2): 210-214. https://doi.org/10.11896/j.issn.1002-137X.2019.02.032

WANG Yong, WANG Yong-dong, DENG Jiang-zhou, ZHANG Pu. Recommendation Algorithm Based on Jensen-Shannon Divergence[J]. Computer Science, 2019, 46(2): 210-214. https://doi.org/10.11896/j.issn.1002-137X.2019.02.032

参考文献

[1]CHOU A Y.The analysis of online social networking:How technology is changing e-commerce purchasing decision[J].International Journal of Information Systems & Change Management,2010,4(4):353-365.
[2]YANG C C.Correlation coefficient evaluation for the fuzzy interval data[J].Journal of Business Research,2016,69(6):2138-2144.
[3]GUAN H,GUAN S,ZHAO A.Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity[J].Symmetry,2017,9(9):191.
[4]TAKACS G,PILASZY I,NEMETH B,et al.Scalable Collaborative Filtering Approaches for Large Recommender System [J].Journal of Machine Learning Research,2009,10:623-656.
[5]KIM H N,JI A T,HA I,et al.Collaborative Filtering Based on Collaborative Tagging for Enhancing the Quality of Recommendation [J].Electronic Commerce Research and Applications,2010,9(1):73-83.
[6]KHROUF H.Hybrid event recommendation using linked data and user diversity[C]∥ ACM Conference on Recommender Systems.ACM,2013:185-192.
[7]MEYMANDPOUR R,DAVIS J G.Recommendations using linked data[C]∥ Proceedings of the 5th Ph.d.Workshop on Information and Knowledge.ACM,2012:75-82.
[8]OSTUNI V C,NOIA T D,SCIASCIO E D,et al.Top-N recom- mendations from implicit feedback leveraging linked open data[C]∥ACM Conference on Recommender Systems.ACM,2013:85-92.
[9]BARJASTEH I,FORSATI R,MASROUR F,et al.Cold-Start Item and User Recommendation with Decoupled Completion and Transduction[C]∥ ACM Conference on Recommender Systems.ACM,2015:91-98.
[10]BINESH N,REZGHI M.A new similarity measure for extraction information from social networks and improve the community detection and recommendation results[C]∥Information and Knowledge Technology.IEEE,2015:146-151.
[11]WANG X M,ZHANG X M,WU Y T,et al.A Collaborative Recommendation Algorithm Based on Heuristic Clustering Modeland Category Similarity[J].Acta Electronica Sinica,2016,44(7):1708-1713.(in Chinese)
王兴茂,张兴明,吴毅涛,等.基于启发式聚类模型和类别相似度的协同过滤推荐算法[J].电子学报,2016,44(7):1708-1713.
[12]WANG Y,DENG J Z,DENG Y H,et al.A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J].New Technology of Library and Information Ser-vice,2016,32(6):73-79.(in Chinese)
王永,邓江洲,邓永恒,等.基于项目概率分布的协同过滤推荐算法[J].现代图书情报技术,2016,32(6):73-79.
[13]PATRA B K,LAUNONEN R,OLLIKAINEN V,et al.A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data[J].Knowledge-Based Systems,2015,82(3):163-177.
[14]MANNING C D.Foundations of statistical natural language processing[M].Massachusetts:MIT Press,1999.
[15]MAJTEY A P,LAMBERTI P W,PRATO D P.Jensen-Shannon divergence as a measure of distinguishability between mixed quantum states[J].Physical Review A,2005,72(5):762-776.
[16]WILLMOTT C J,MATSUURA K.Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error(RMSE) in Assessing Average Model Performance[J].Climate Research,2005,30(1):79-82.

相关文章 15

[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]	孙晓寒, 张莉. 基于评分区域子空间的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace 计算机科学, 2022, 49(7): 50-56. https://doi.org/10.11896/jsjkx.210600062
[4]	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
[5]	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
[6]	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
[7]	王美玲, 刘晓楠, 尹美娟, 乔猛, 荆丽娜. 基于评论和物品描述的深度学习推荐算法 Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions 计算机科学, 2022, 49(3): 99-104. https://doi.org/10.11896/jsjkx.210200170
[8]	董晓梅, 王蕊, 邹欣开. 面向推荐应用的差分隐私方案综述 Survey on Privacy Protection Solutions for Recommended Applications 计算机科学, 2021, 48(9): 21-35. https://doi.org/10.11896/jsjkx.201100083
[9]	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
[10]	邵超, 宋淑米. 基于信任关系下用户兴趣偏好的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship 计算机科学, 2021, 48(6A): 240-245. https://doi.org/10.11896/jsjkx.200700113
[11]	武建新, 张志鸿. 融合用户评分与显隐兴趣相似度的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest 计算机科学, 2021, 48(5): 147-154. https://doi.org/10.11896/jsjkx.200300072
[12]	张岩金, 白亮. 一种基于符号关系图的快速符号数据聚类算法 Fast Symbolic Data Clustering Algorithm Based on Symbolic Relation Graph 计算机科学, 2021, 48(4): 111-116. https://doi.org/10.11896/jsjkx.200800011
[13]	肖诗涛, 邵蓥侠, 宋卫平, 崔斌. 面向协同过滤推荐的新型混合评分函数 Hybrid Score Function for Collaborative Filtering Recommendation 计算机科学, 2021, 48(3): 113-118. https://doi.org/10.11896/jsjkx.200900067
[14]	郝志峰, 廖祥财, 温雯, 蔡瑞初. 基于多上下文信息的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Multi-context Information 计算机科学, 2021, 48(3): 168-173. https://doi.org/10.11896/jsjkx.200700101
[15]	韩立锋, 陈莉. 融合用户属性与项目流行度的用户冷启动推荐模型 User Cold Start Recommendation Model Integrating User Attributes and Item Popularity 计算机科学, 2021, 48(2): 114-120. https://doi.org/10.11896/jsjkx.200900152

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

融合Jensen-Shannon散度的推荐算法

Recommendation Algorithm Based on Jensen-Shannon Divergence

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0