Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem

Keunho Choi, Yongmoo Suh, Donghee Yoo


Many online shopping malls have implemented personalized recommendation systems to improve customer retention in the age of high competition and information overload. Sellers make use of these recommendation systems to survive high competition and buyers utilize them to find proper product information for their own needs. However, transaction data of most online shopping malls prevent us from using collaborative filtering (CF) technique to recommend products, for the following two reasons: 1) explicit rating information is rarely available in the transaction data; 2) the sparsity problem usually occurs in the data, which makes it difficult to identify reliable neighbors, resulting in less effective recommendations. Therefore, this paper first suggests a means to derive implicit rating information from the transaction data of an online shopping mall and then proposes a new user similarity function to mitigate the sparsity problem. The new user similarity function computes the user similarity of two users if they rated similar items, while the user similarity function of traditional CF technique computes it only if they rated common items. Results from several experiments using an online shopping mall dataset in Korea demonstrate that our approach significantly outperforms the traditional CF technique.


recommendation system, collaborative filtering, sparsity problem, similarity function

Full Text:



M. Pazzani, D. Billsus (1997); Learning and Revising User Profile: The Identification of Interesting Web Sites, Machine Learning, 27(3): 313-331.

T. Zhang, R. Agarwal, H.C. Lucas (2011); The Value of IT-Enabled Retailer Learning: Personalized Product Recommendations and Customer Store Loyalty in Electronic Markets, MIS Quarterly, 35(4): 859-881.

Y. Jing, H. Liu (2013); A Model for Collaborative Filtering Recommendation in E-Commerce Environment, International Journal of Computers Communications and Control, 8(4): 560- 570.

D.R. Liu, C.H. Lai, W.J. Lee (2009); A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation, Information Sciences, 179(20): 3505-3519.

M. Balabanovic, Y. Shoham (1998); Content-Based, Collaborative Recommendation, Communications of the ACM, 40(3): 66-72.

K. Lang (1995); NewsWeeder: Learning to Filter Netnews, Pro. of the 12th Int. Conference on Machine Learning.

U. Shardanand, P. Maes (1995); Social Information Filtering Algorithms for Automating "Word of Mouth", Pro. of the SIGCHI Conference on Human Factors in Computing Systems.

G. Adomavicius, A. Tuzhilin (2005); Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, 17(6): 734-749.

D. Billsus, M.J. Pazzani (1998); Learning Collaborative Information Filters, Pro. of the 15th Int. Conference on Machine Learning.

V. Formoso, D. Fernandez, F. Cacheda, V. Carneiro (2012); Using Profile Expansion Techniques to Alleviate the New User Problem, Information Processing and Management, 49(3): 659-672.

H.N. Kim, A.T. Ji, I. Ha, G.S. Jo (2010); Collaborative Filtering based on Collaborative Tagging for Enhancing the Quality of Recommendation, Electronic Commerce Research and Applications, 9(1): 73-83.

T.Q. Lee, Y. Park, Y.T. Park (2008); A Time-based Approach to Effective Recommender Systems Using Implicit Feedback, Expert Systems with Applications, 34(4): 3055-3062.

Q. Shambour, J. Lu (2011); A Hybrid Trust-Enhanced Collaborative Filtering Recommendation Approach for Personalized Government-to-Business e-Services, International Journal of Intelligent Systems, 26(9): 814-843.

C.C. Aggarwal, C. Procopiuc, P.S. Yu (2002); Finding Localized Associations in Market Basket Data. IEEE Transactions on Knowledge and Data Engineering, 14(1): 51-62.

C.L. Huang, W.L. Huang (2009); Handling Sequential Pattern Decay: Developing a Two- Stage Collaborative Recommendation System, Electronic Commerce Research and Applications, 8(3): 117-129.

Y. Wang, W. Dai, Y. Yuan (2008); Website Browsing Aid: A Navigation Graph-based Recommendation System, Decision Support Systems, 45(3): 387-400.

D. Goldberg, D. Nichols, B.M. Oki, D. Terry (1992); Using Collaborative Filtering to Weave an Information Tapestry, Communications of the ACM, 35(12): 61-70.

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, J. Riedl (1994); GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Pro. of the 1994 ACM Conference on Computer Supported Cooperative Work.

G. Adomavicius, Y. Kwon (2007); New Recommendation Techniques for Multicriteria Rating Systems, IEEE Intelligent Systems, 22(3): 48-55.

I.S. Altingovde, O.N. Subakan, O. Ulusoy (2012); Cluster Searching Strategies for Collaborative Recommendation Systems, Information Processing and Management, 49(3): 688-697.

K.W. Cheung, J.T. Kwok, M.H. Law, K.C. Tsui (2003); Mining Customer Product Ratings for Personalized Marketing, Decision Support Systems, 35(2): 231-243.

K. Goldberg, T. Roeder, D. Gupta, C. Perkins (2001); Eigentaste: A Constant Time Collaborative Filtering Algorithm, Information Retrieval, 4(2): 133-151.

Y. Koren (2010); Factor in the Neighbors: Scalable and Accurate Collaborative Filtering, ACM Transactions on Knowledge Discovery from Data, 4(1): 1-24.

R. Salakhutdinov, N. Srebro (2010); Collaborative Filtering in a Non-UniformWorld: Learning with the Weighted Trace Norm, arXiv:1002.2780v1, 1-9.

D. Joaquin, I. Naohiro (1999); Memory-based Weighted-Majority Prediction. Pro. of ACM SIGIR' 99 Workshop on Recommender Systems: Algorithms and Evaluation.

J. Lee, S. Lee, H. Kim (2011); An Probabilistic Approach to Semantic Collaborative Filtering Using World Knowledge, Journal of Information Science, 37(1): 49-66.

J.M. Yang, K.F. Li, D.F. Zhang (2009); Recommendation based on Rational Inferences in Collaborative Filtering, Knowledge-Based Systems, 22(1): 105-114.

Z. Liu, W. Qu, H. Li, C. Xie (2010); A Hybrid Collaborative Filtering Recommendation Mechanism for P2P Networks, Future Generation Computer Systems, 26(8): 1409-1417.

DOI: https://doi.org/10.15837/ijccc.2016.5.2152

Copyright (c) 2017 Keunho Choi, Yongmoo Suh, Donghee Yoo

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

IJCCC is an Open Access Journal : CC-BY-NC.

Articles published in IJCCC user license are protected by copyright.

Users can access, download, copy, translate the IJCCC articles for non-commercial purposes provided that users, but cannot redistribute, display or adapt:

  • Cite the article using an appropriate bibliographic citation: author(s), article title, journal, volume, issue, page numbers, year of publication, DOI, and the link to the definitive published version on IJCCC website;
  • Maintain the integrity of the IJCCC article;
  • Retain the copyright notices and links to these terms and conditions so it is clear to other users what can and what cannot be done with the  article;
  • Ensure that, for any content in the IJCCC article that is identified as belonging to a third party, any re-use complies with the copyright policies of that third party;
  • Any translations must prominently display the statement: "This is an unofficial translation of an article that appeared in IJCCC. Agora University  has not endorsed this translation."

This is a non commercial license where the use of published articles for commercial purposes is forbiden. 

Commercial purposes include: 

  • Copying or downloading IJCCC articles, or linking to such postings, for further redistribution, sale or licensing, for a fee;
  • Copying, downloading or posting by a site or service that incorporates advertising with such content;
  • The inclusion or incorporation of article content in other works or services (other than normal quotations with an appropriate citation) that is then available for sale or licensing, for a fee;
  • Use of IJCCC articles or article content (other than normal quotations with appropriate citation) by for-profit organizations for promotional purposes, whether for a fee or otherwise;
  • Use for the purposes of monetary reward by means of sale, resale, license, loan, transfer or other form of commercial exploitation;

    The licensor cannot revoke these freedoms as long as you follow the license terms.

[End of CC-BY-NC  License for Website User]

INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.

IJCCC was founded in 2006,  at Agora University, by  Ioan DZITAC (Editor-in-Chief),  Florin Gheorghe FILIP (Editor-in-Chief), and  Misu-Jan MANOLESCU (Managing Editor).

Ethics: This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Ioan  DZITAC (Editor-in-Chief) at COPE European Seminar, Bruxelles, 2015:

IJCCC is covered/indexed/abstracted in Science Citation Index Expanded (since vol.1(S),  2006); JCR2018: IF=1.585..

IJCCC is indexed in Scopus from 2008 (CiteScore2018 = 1.56):

Nomination by Elsevier for Journal Excellence Award Romania 2015 (SNIP2014 = 1.029): Elsevier/ Scopus

IJCCC was nominated by Elsevier for Journal Excellence Award - "Scopus Awards Romania 2015" (SNIP2014 = 1.029).

IJCCC is in Top 3 of 157 Romanian journals indexed by Scopus (in all fields) and No.1 in Computer Science field by Elsevier/ Scopus.


 Impact Factor in JCR2018 (Clarivate Analytics/SCI Expanded/ISI Web of Science): IF=1.585 (Q3). Scopus: CiteScore2018=1.56 (Q2);

SCImago Journal & Country Rank

Editors-in-Chief: Ioan DZITAC & Florin Gheorghe FILIP.