[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.
王永, 王永东, 郭慧芳, 周玉敏. 一种基于离散增量的项目相似性度量方法*[J]. 数据分析与知识发现, 2018, 2(5): 70-76.
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.
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.
(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.
(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
(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