Computer Science ›› 2019, Vol. 46 ›› Issue (2): 210-214.doi: 10.11896/j.issn.1002-137X.2019.02.032

• Artificial Intelligence • Previous Articles     Next Articles

Recommendation Algorithm Based on Jensen-Shannon Divergence

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

  1. 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

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

CLC Number: 

  • TP391
[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.
[1] CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning [J]. Computer Science, 2022, 49(9): 1-13.
[2] WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54.
[3] SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56.
[4] CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241.
[5] HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279.
[6] GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao. Hybrid Recommender System Based on Attention Mechanisms and Gating Network [J]. Computer Science, 2022, 49(6): 158-164.
[7] WANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na. Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions [J]. Computer Science, 2022, 49(3): 99-104.
[8] DONG Xiao-mei, WANG Rui, ZOU Xin-kai. Survey on Privacy Protection Solutions for Recommended Applications [J]. Computer Science, 2021, 48(9): 21-35.
[9] ZHAN Wan-jiang, HONG Zhi-lin, FANG Lu-ping, WU Zhe-fu, LYU Yue-hua. Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning [J]. Computer Science, 2021, 48(7): 172-177.
[10] SHAO Chao, SONG Shu-mi. Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship [J]. Computer Science, 2021, 48(6A): 240-245.
[11] HUANG Ming, SUN Lin-fu, REN Chun-hua , WU Qi-shi. Improved KNN Time Series Analysis Method [J]. Computer Science, 2021, 48(6): 71-78.
[12] WU Jian-xin, ZHANG Zhi-hong. Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest [J]. Computer Science, 2021, 48(5): 147-154.
[13] ZHANG Yan-jin, BAI Liang. Fast Symbolic Data Clustering Algorithm Based on Symbolic Relation Graph [J]. Computer Science, 2021, 48(4): 111-116.
[14] XIAO Shi-tao, SHAO Ying-xia, SONG Wei-ping, CUI Bin. Hybrid Score Function for Collaborative Filtering Recommendation [J]. Computer Science, 2021, 48(3): 113-118.
[15] WANG Xing , KANG Zhao. Smooth Representation-based Semi-supervised Classification [J]. Computer Science, 2021, 48(3): 124-129.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!