Computer Science ›› 2020, Vol. 47 ›› Issue (2): 31-36.doi: 10.11896/jsjkx.190500130

• Database & Big Data & Data Science • Previous Articles     Next Articles

New Similarity Measure Based on Extremely Rating Behavior

FENG Chen-jiao1,2,LIANG Ji-ye1,SONG Peng3,WANG Zhi-qiang1   

  1. (Key Laboratory of Computation Intelligence & Chinese Information Processing (Shanxi University),Ministry of Education,Taiyuan 030006,China)1;
    (College of Applied Mathematics,Shanxi University of Finance and Economics,Taiyuan 030006,China)2;
    (School of Economics and Management,Shanxi University,Taiyuan 030006,China)3
  • Received:2019-05-23 Online:2020-02-15 Published:2020-03-18
  • About author:FENG Chen-jiao,born in 1977,doctorial student,lecturer,is member of China Computer Federation.Her main research interests include data mining,big data correlation analysis and recommender systems;LIANG Ji-ye,born in 1962,Ph.D,professor,Ph.D supervisor,is member of China Computer Federation.His main research interests include granular computing,data mining and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61876103), Projects of Key Research and Development Plan of Shanxi Province (201603D111014), Research Project Supported by Shanxi Scholarship Council of China (2017-005) and 1331 Engineering Project of Shanxi Province, China.

Abstract: With the rapid development of Internet technology,drastic Internet information explosion makes information overload as an increasingly serious problem.Faced with the massive Internet information,users consume a lot of time to search for information or products,but the search solution is constrained.The recommender systems is hence proposed to address the problem of information overload.The recommender systems use users’ historical behaviors to speculate their needs,interests,etc.,and recommend the information and products users may be interested in.As an important type of recommendation approach,the memory-based collaborative filtering methods establish the rating prediction function based on neighbor information of the user or pro-duct.The essence of the function is to precisely measure the similarity between users or products.The traditional similarity mea-sures such as Pearson,Cosin and Spearman rank correlation coefficients,only take into account the linear relationship between users,while the heuristic similarities,such as the PIP measurement based on three special factors and its improved version,only depict the non-liner relationship between users.Indeed,in the recommender systems,it is neither the linear relation nor the non-linear relation is good for measuring the similarity between users.In order to describe the similarity among users more finely,this paper proposed a similarity measure index of the correlation level considering the extreme rating behaviors based on anonli-near function.By integrating this index with the traditional linear correlation coefficients,this paper constructed a novel similarity measure.Comparative experiments were conducted to test the practicability and validity of the proposed approach on Ml (100k) and Ml-latest-small datasets.The results demonstrate that the proposed method performs better judged by indicators of MAE and RMSE.

Key words: Recommender systems, Collaborative filtering, Memory-based collaborative filtering, Extremely rating behavior, Similarity

CLC Number: 

  • TP182
[1]GOLDBERG D,NICHOLS D,OKIB M,et al.Using collaborative filtering to weave an information tapestry[J].Communications of ACM,1992,35(12):61-70.
[2]RESNICK P,VARIAN H R.Recommender systems[J].Communications of ACM,1997,40(3):56-58.
[3]ZENEBE A,NORCIO A F.Representation:Similarity measures and aggregation methods using fuzzy sets for content-based re-commender systems[J].Fuzzy Sets and Systems,2009,160(1):76-94.
[4]SCHAFER J B,KONSTAN J A,RIEDL J.E-commerce recommendation applications[J].Data Mining and Knowledge Disco-very,2001,5(1/2):115-153.
[5]BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recom-mender systems survey[J].Knowledge-Based Systems,2013,46(1):109-132.
[6]AAMIR M,BHUSRY M.Recommendation system:State of the art approach[J].International Journal of Computer Applications,2015,120:25-32.
[7]XIAO Y Y,ZHANG H Y.Friend recommendation method based on users’latent features in social networks[J].Computer Science,2018,45(3):220-254.
[8]ZHANG S,YAO L,SUN A,et al.Deep learning based recommender system:A survey and new perspectives [J].ACM Computing Surveys,2017,1(1):1-35.
[9]HANG L V,JIANG B T,LV S Y,et al.Survey on deep learning based recommender systems[J].Chinese Journal of Computers,2018,41(7):191-219.
[10]HSU C C,YEH M Y,LIN S D.A general framework for impli-cit and explicit social recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2018,14(8):1-14.
[11]KATZMAN J,SHAHAM U,BATES J,et al.DeepSurv:perso-nalized treatment recommender system using a cox proportional hazards deep neural network[J].Bmc Medical Research Metho-dology,2016,18(1):24.
[12]QUADRANA M,CREMONESI P,JANNACH D.Sequence-aware recommender systems[J].ACM Computing Surveys,2018,51(4):373-374.
[13]BREESE J S,HECKERMAN D,KADIE C.Empirical analysis of predictive algorithms for collaborative filtering[J].Uncertainty in Artificial Intelligence,2013,98(7):43-52.
[14]SU X Y,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques[J].Advances in Artificial Intelligence,2012,2009(12):1-19.
[15]SHI Y,LARSON M,HANJALIC A.Collaborative filtering beyond the user-item matrix:A survey of the state of the art and future challenges[J].ACM Computing Surveys,2014,47(1):1-45.
[16]LEE S.Using entropy for similarity measures in collaborative filtering[J/OL].Journal of Ambient Intelligence and Humanized Computing,2019.
[17]HE X,HE Z,SONG J,et al.NAIS:Neural attentive item similarity model for recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(12):2354-2366.
[18]LIAN D,GE Y,ZHANG F,et al.Scalable content-aware colla-borative filtering for location recommendation[J].IEEE Transa-ctions on Knowledge and Data Engineering,2018,30(6):1122- 1135.
[19]SARWAR B M,KARYPIS G,KONSTAN J A,et al. Analysis of recommendation algorithms for e-commerce[C]∥Proceedings of ACM E-Commerce.Minneapolis,Minn,USA,2000:158-167.
[20]RESNICK P,IACOVOU N,SUCHAK M,et al.Grouplens:An open architecture for collaborative filtering of netnews[C]∥Proceedings of the ACM Conference on Computer Supported Cooperative Work.New York:ACM Press,1994:175-186.
[21]SHARDANAND U,MAES P.Social information filtering:algorithm for automating’ word of mouth’[C]∥Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems.New York:ACM Press,1995:210-217.
[22]KENDALL M G.Rank correlation methods[J].British Journal of Psychology,1990,25(1):86-91.
[23]AHN H J.A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem[J].Information Sciences,2008,178(1):37-51.
[24]LIU H,ZHENG H,MIAN A,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge-Based Systems,2014,56(3):156-166.
[25]HERLOCKER J L,KONSTAN J A,BORCHERS A,et al.An algorithmic framework for performing collaborative filtering[C]∥Proceedings of the SIGIR ’99 International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM Press,1999:230-237.
[26]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.New York:ACM Press,2009:397-406.
[1] HU Ping, QIN Ke-yun. Similarity Construction Method for Pythagorean Fuzzy Set Based on Fuzzy Equivalence [J]. Computer Science, 2021, 48(1): 152-156.
[2] MA Li-bo, QIN Xiao-lin. Topic-Location-Category Aware Point-of-interest Recommendation [J]. Computer Science, 2020, 47(9): 81-87.
[3] LIU Jing, FANG Xian-wen. Mining Method of Business Process Change Based on Cost Alignment [J]. Computer Science, 2020, 47(7): 78-83.
[4] LI Zhang-wei, XIAO Lu-qian, HAO Xiao-hu, ZHOU Xiao-gen, ZHANG Gui-jun. Multimodal Optimization Algorithm for Protein Conformation Space [J]. Computer Science, 2020, 47(7): 161-165.
[5] WANG Meng, DING Zhi-jun. New Device Fingerprint Feature Selection and Model Construction Method [J]. Computer Science, 2020, 47(7): 257-262.
[6] LUO Jia-lei and MENG Li-min. Signal Timing Scheme Recommendation Algorithm Based on Intersection Similarity [J]. Computer Science, 2020, 47(6A): 66-69.
[7] LI Jin-xia, ZHAO Zhi-gang, LI Qiang, LV Hui-xian and LI Ming-sheng. Improved Locality and Similarity Preserving Feature Selection Algorithm [J]. Computer Science, 2020, 47(6A): 480-484.
[8] ZOU Hai-tao, ZHENG Shang, WANG Qi, YU Hua-long and GAO Shang. Adaptive High-order Rating Distance Recommendation Model Based on Newton Optimization [J]. Computer Science, 2020, 47(6A): 494-499.
[9] SHU Yun-feng and WANG Zhong-qing. Research on Chinese Patent Summarization Based on Patented Structure [J]. Computer Science, 2020, 47(6A): 45-48.
[10] MO Cai-wang, CHANG Kan, LI Heng-xin, LI Ming-hong, QIN Tuan-fa. Color Image Super-resolution Algorithm Based on Inter-channel Correlation and Nonlocal Self-similarity [J]. Computer Science, 2020, 47(6): 138-143.
[11] YUAN Rong, SONG Yu-rong, MENG Fan-rong. Link Prediction Method Based on Weighted Network Topology Weight [J]. Computer Science, 2020, 47(5): 265-270.
[12] ZHU Lei, HU Qin-han, ZHAO Lei, YANG Ji-wen. Collaborative Filtering Algorithm Based on Rating Preference and Item Attributes [J]. Computer Science, 2020, 47(4): 67-73.
[13] ZHAO Nan, PI Wen-chao, XU Chang-qiao. Video Recommendation Algorithm for Multidimensional Feature Analysis and Filtering [J]. Computer Science, 2020, 47(4): 103-107.
[14] ZHANG Yun-fan,ZHOU Yu,HUANG Zhi-qiu. Semantic Similarity Based API Usage Pattern Recommendation [J]. Computer Science, 2020, 47(3): 34-40.
[15] ZHONG Ya,GUO Yuan-bo,LIU Chun-hui,LI Tao. User Attributes Profiling Method and Application in Insider Threat Detection [J]. Computer Science, 2020, 47(3): 292-297.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .