Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (5): 70-76    DOI: 10.11925/infotech.2096-3467.2017.1019
Orginal Article Current Issue | Archive | Adv Search |
Measuring Item Similarity Based on Increment of Diversity
Wang Yong(), Wang Yongdong, Guo Huifang, Zhou Yumin
Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Download: PDF (657 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      

[Objective] This study aims to solve the issues facing traditional methods measuring item similarity, such as using common rating and poor prediction accuracy in highly sparse data environment. [Methods] First, we constructed the dissimilarity coefficient with the increment of diversity from bioinformatics. Then, we calculated item similarity according to the frequency and distribution of ratings, which effectively addressed the data sparsity issue. Finally, we improved the accuracy of measurement with the item attributes. [Results] Compared with traditional algorithms, the proposed method reduced RMSE by 2.56%, and then increased the F value by 3.88%. [Limitations] The diversity of our recommendation might be insufficient. [Conclusions] The proposed method could effectively measure item similarity.

Key wordsIncrement of Diversity      Similarity Measure      Data Sparsity      Collaborative Filtering      Cold-Start     
Received: 11 October 2017      Published: 20 June 2018
ZTFLH:  TP391  

Cite this article:

Wang Yong,Wang Yongdong,Guo Huifang,Zhou Yumin. Measuring Item Similarity Based on Increment of Diversity. Data Analysis and Knowledge Discovery, 2018, 2(5): 70-76.

URL:     OR

[1] Schafer J B, Konstan J, Riedl J.Recommender Systems in E-commerce[C]// Proceedings of the 1st ACM Conference on Electronic Commerce. ACM, 1999: 158-166.
[2] Sánchez-Moreno D, González A B G, Vicente M D M, et al. A Collaborative Filtering Method for Music Recommendation Using Playing Coefficients for Artists and Users[J]. Expert Systems with Applications, 2016, 66(C): 234-244.
doi: 10.1016/j.eswa.2016.09.019
[3] 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.
[4] Ortega F, Sánchez J L, Bobadilla J, et al.Improving Collaborative Filtering-based Recommender Systems Results Using Pareto Dominance[J]. Information Sciences, 2013, 239(4): 50-61.
doi: 10.1016/j.ins.2013.03.011
[5] Liu H, Hu Z, 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.
doi: 10.1016/j.knosys.2013.11.006
[6] Sarwar S M, Hasan M, Billal M, et al.Similarity Aggregation for Collaborative Filtering[C]// Proceedings of International Conference on Analysis of Images, Social Networks and Texts. Springer, 2015: 236-242.
[7] Ji K, Shen H.Addressing Cold-Start: Scalable Recommendation with Tags and Keywords[J]. Knowledge-Based Systems, 2015, 83(1): 42-50.
doi: 10.1016/j.knosys.2015.03.008
[8] Guan C, Yuen K K F, Coenen F. Towards an Intuitionistic Fuzzy Agglomerative Hierarchical Clustering Algorithm for Music Recommendation in Folksonomy[C]// Proceedings of 2015 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2016: 2039-2042.
[9] 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.
doi: 10.3969/j.issn.0372-2112.2016.07.027
[9] (Wang Xingmao, Zhang Xingming, Wu Yitao, et al.A Collaborative Recommendation Algorithm Based on Heuristic Clustering Model and Category Similarity[J]. Acta Electronica Sinica, 2016, 44(7): 1708-1713.)
doi: 10.3969/j.issn.0372-2112.2016.07.027
[10] Du Y P, Yao C Q, Huo S H, et al.A New Item-based Deep Network Structure Using a Restricted Boltzmann Machine for Collaborative Filtering[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(5): 658-666.
[11] Kabbur S, Ning X, Karypis G.FISM: Factored Item Similarity Models for Top-N Recommender Systems[C]// Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2013: 659-667.
[12] 黄波, 严宣辉, 林建辉. 基于联合非负矩阵分解的协同过滤推荐算法[J]. 模式识别与人工智能, 2016, 29(8): 725-734.
[12] (Huang Bo, Yan Xuanhui, Lin Jianhui.Collaborative Filtering Recommendation Algorithm Based on Joint Nonnegative Matrix Factorization[J]. Pattern Recognition and Artificial Intelligence, 2016, 29(8): 725-734.)
[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(C): 163-177.
doi: 10.1016/j.knosys.2015.03.001
[14] 于阳, 于洪涛, 黄瑞阳. 基于熵优化近邻选择的协同过滤推荐算法[J]. 计算机应用研究, 2017, 34(9): 2618-2623.
[14] (Yu Yang, Yu Hongtao, Huang Ruiyang.Collaborative Filtering Recommendation Algorithm Based on Entropy Optimization Nearest-Neighbor Selection[J]. Application Research of Computers, 2017, 34(9): 2618-2623.)
[15] Wang Y, Deng J, Gao J, et al. A Hybrid User Similarity Model for Collaborative Filtering[J]. Information Sciences, 2017, 418-419: 102-118.
doi: 10.1016/j.ins.2017.08.008
[16] Chen Y L, Li Q Z, Zhang L Q.Using Increment of Diversity to Predict Mitochondrial Proteins of Malaria Parasite: Integrating Pseudo-amino Acid Composition and Structural Alphabet[J]. Amino Acids, 2012, 42(4): 1309-1316.
doi: 10.1007/s00726-010-0825-7 pmid: 21191803
[17] Ellingsen K E, Clarke K R, Somerfield P J, et al.Taxonomic Distinctness as a Measure of Diversity Applied over a Large Scale: The Benthos of the Norwegian Continental Shelf[J]. Journal of Animal Ecology, 2005, 74(6): 1069-1079.
doi: 10.1111/j.1365-2656.2005.01004.x
[18] Zuo Y C, Li Q Z.Using K-minimum Increment of Diversity to Predict Secretory Proteins of Malaria Parasite Based on Groupings of Amino Acids[J]. Amino Acids, 2010, 38(3): 859-867.
doi: 10.1007/s00726-009-0292-1 pmid: 19387791
[19] 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.
doi: 10.3354/cr030079
[20] Goutte C, Gaussier E.A Probabilistic Interpretation of Precision, Recall and F -Score, with Implication for Evaluation[C]// Proceedings of European Conference on Information Retrieval. Springer Berlin Heidelberg, 2005: 345-359.
[21] 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.
doi: 10.1016/j.ins.2007.07.024
[1] Li Zhenyu, Li Shuqing. Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups[J]. 数据分析与知识发现, 2021, 5(11): 124-134.
[2] Yang Chen, Chen Xiaohong, Wang Chuhan, Liu Tingting. Recommendation Strategy Based on Users’ Preferences for Fine-Grained Attributes[J]. 数据分析与知识发现, 2021, 5(10): 94-102.
[3] Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[4] Su Qing,Chen Sizhao,Wu Weimin,Li Xiaomei,Huang Tiankuan. Personalized Recommendation Model Based on Collaborative Filtering Algorithm of Learning Situation[J]. 数据分析与知识发现, 2020, 4(5): 105-117.
[5] Zheng Songyin,Tan Guoxin,Shi Zhongchao. Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[6] Ding Yong,Chen Xi,Jiang Cuiqing,Wang Zhao. Predicting Online Ratings with Network Representation Learning and XGBoost[J]. 数据分析与知识发现, 2020, 4(11): 52-62.
[7] Fusen Jiao,Shuqing Li. Collaborative Filtering Recommendation Based on Item Quality and User Ratings[J]. 数据分析与知识发现, 2019, 3(8): 62-67.
[8] Shan Li,Yehui Yao,Hao Li,Jie Liu,Karmapemo. ISA Biclustering Algorithm for Group Recommendation[J]. 数据分析与知识发现, 2019, 3(8): 77-87.
[9] Sun Haixia,Wang Lei,Wu Yingjie,Hua Weina,Li Junlian. Matching Strategies for Institution Names in Literature Database[J]. 数据分析与知识发现, 2018, 2(8): 88-97.
[10] Li Jie,Yang Fang,Xu Chenxi. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns[J]. 数据分析与知识发现, 2018, 2(7): 72-80.
[11] Wang Daoping,Jiang Zhongyang,Zhang Boqing. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor[J]. 数据分析与知识发现, 2018, 2(6): 102-109.
[12] Hua Lingfeng,Yang Gaoming,Wang Xiujun. Recommending Diversified News Based on User’s Locations[J]. 数据分析与知识发现, 2018, 2(5): 94-104.
[13] Xue Fuliang,Liu Junling. Improving Collaborative Filtering Recommendation Based on Trust Relationship Among Users[J]. 数据分析与知识发现, 2017, 1(7): 90-99.
[14] Qin Xingxin,Wang Rongbo,Huang Xiaoxi,Chen Zhiqun. Slope One Collaborative Filtering Algorithm Based on Multi-Weights[J]. 数据分析与知识发现, 2017, 1(6): 65-71.
[15] Li Daoguo,Li Lianjie,Shen Enping. New Collaborative Filtering Recommendation Algorithm Based on User Rating Time[J]. 现代图书情报技术, 2016, 32(9): 65-69.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938