Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Dan Chang, Hao Yu Gui, Rui Fan, Ze Zhou Fan, Ji Tian


Problems such as low recommendation precision and efficiency often exist in traditional collaborative filtering because of the huge basic data volume. In order to solve these problems, we proposed a new algorithm which combines collaborative filtering and support vector machine (SVM). Different with traditional collaborative filtering, we used SVM to classify commodities into positive and negative feedbacks. Then we selected the commodities that have positive feedback to calculate the comprehensive grades of marks and comments. After that, we build SVM-based collaborative filtering algorithm. Experiments on Taobao data (a Chinese online shopping website owned by Alibaba) showed that the algorithm has good recommendation precision and recommendation efficiency, thus having certain practical value in the E-commerce industry.


recommendation precision, recommendation efficiency, support vector machine (SVM), collaborative filtering

Full Text:



Ahmad, A.S.; Hassan, M.Y.; Abdullah, M.P. et al. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting, Renewable and Sustainable Energy Reviews, 33, 102-109, 2014.

Barbieri, N. (2013). An Analysis of Probabilistic Methods for Top-N Recommendation in Collaborative Filtering, Machine Learning & Knowledge Discovery in Databases-European Conference, DBLP, 2013.

Cheng, Q; Wang X; Yin, D. et al. (2015); The New Similarity Measure Based on User Preference Models for Collaborative Filtering, IEEE International Conference on Information & Automation, IEEE, 2015.

Chung, Y; Jung, H.W.; Kim, J. et al. (2013). Personalized Expert-Based Recommender System: Training C-SVM for Personalized Expert Identification, International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer, Berlin, Heidelberg, 2013.

Du, Y,-P.; Yao, C.-Q.; Huo, S.-H. et al. (2017). A new item-based deep network structure using a restricted Boltzmann machine for collaborative filtering, Frontiers of Information Technology & Electronic Engineering, 18(05), 658-666, 2017.

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

Guo, G.; Zhang, J.; Thalmann, D. (1992). Merging trust in collaborative filtering to alleviate data sparsity and cold start, Knowledge-Based Systems, 35, 57-68, 2014.

Hu, Y.; Peng, Q.; Hu, X. et al. (1992). Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering, IEEE Transactions on Services Computing, 8(5), 782-794, 2015.

Jindal, A.; Dua, A.; Kaur, K. et al. (2016). Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid, IEEE Transactions on Industrial Informatics, 12(3), 1005-1016, 2016.

Li, G.; Ou, W. (2016). Pairwise probabilistic matrix factorization for implicitfeedback collaborative filtering, Neurocomputing, 204, 17-25, 2016.

Li, H.; Hong, R.; Lian, D. et al. (2016). A Relaxed Ranking-Based Factor Model for Recommender System from Implicit Feedback, IJCAI, 1683-1689, 2016.

Li, Z.; Peng, J.Y.; Geng, G.H. et al. (2015). Video recommendation based on multi-modal information and multiple kernel, Multimedia Tools and Applications, 74(13), 4599-4616, 2015.

Liu, X. (2017). A collaborative filtering recommendation algorithm based on the influence sets of e-learning group's behavior, Cluster Computing, 1-11, 2017.

Madadipouya, K. (2015). A Location-Based Movie Recommender System Using Collaborative Filtering, Computer Science, 5, 2015.

Manek, A.S.; Shenoy, P.D.; Mohan, M.C. et al. (2017). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier, World Wide Web, 20, 135-154, 2017.

Nilashi, M.; Ibrahim, O.B.; Ithnin, N. et al. (2015); A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques, Soft Computing, 19(11), 3173- 3207, 2015.

Nilashi, M.; Ibrahim, O.B.; Ithnin, N. (2014). Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system, Knowledge-Based Systems, 60(2), 82-101, 2014.

Nasiri, M.; Minaei, B. (2016). Increasing prediction accuracy in collaborative filtering with initialized factor matrices, Journal of Supercomputing, 72(6), 2157-2169, 2016.

Ren, L.; Wang, W. (2017). An SVM-based collaborative filtering approach for Top-N web services recommendation, Future Generation Computer Systems, S0167739X17300389, 2017.

Sedhain, S.; Sanner, S.; Braziunas, D. et al. (2014). Social collaborative filtering for coldstart recommendations, 345-348,2014.

Selakov, A.; Cvijetinovi, D.; Milovi, L. et al. (2014). Hybrid PSO-SVM method for shortterm load forecasting during periods with significant temperature variations in city of Burbank, Applied Soft Computing, 16, 80-88, 2014.

Su, H.; Lin, X.; Yan, B. et al. (2015). The Collaborative Filtering Algorithm with Time Weight Based on Map Reduce, International Conference on Big Data Computing & Communications, Springer, Cham, 2015.

Uricar, M.; Timofte, R.; Rothe, R. et al. (2016); Structured Output SVM Prediction of Apparent Age, Gender and Smile From Deep Features, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016.

Wang, Z.; Liu, Y.; Chiu, S. (2016). An efficient parallel collaborative filtering algorithm on multi-GPU platform, The Journal of Supercomputing, 72(6), 2080-2094, 2016.

Wei, J.; He, J.; Chen, K. et al. (2017); Collaborative filtering and deep learning based recommendation system for cold start items, Expert Systems with Applications, 69,29-39,2017.

Yagci, A.M.; Aytekin, T.; Gurgen, F.S. (2017). Scalable and adaptive collaborative filtering by mining frequent item co-occurrences in a user feedback stream, Engineering Applications of Artificial Intelligenceert Systems with Applications, 58,2017.

Zhang, F.; Gong, T.; Lee V.E. et al. (2016). Fast algorithms to evaluate collaborative filtering recommender systems, Knowledge-Based Systems, 96(C), 96-103, 2016.

Zhang, D.W.; Xu, H.; Su, Z. et al. (2015). Chinese comments sentiment classification based on word2vec and SVM perf, Expert Systems with Applications, 42(4), 1857-1863, 2015.

Zhao, P.X.; Gao, W.; Han, X. et al. (2019). Bi-objective collaborative scheduling optimization of airport ferry vehicle and tractor, International Journal of Simulation Modelling, 18(2), 355-365,2019.

Zhao, P.X.; Luo, W.H.; Han, X. (2019). Time-dependent and bi-objective vehicle routing problem with time windows, Advances in Production Engineering & Management, 14(2), 201-212,2019.

Zhou, W.; Wen, J.; Gao, M. et al. (2015). A Shilling Attack Detection Method Based on SVM and Target Item Analysis in Collaborative Filtering Recommender Systems, International Conference on Knowledge Science, 2015.



Copyright (c) 2019 Dan Chang, Hao Yu Gui, Rui Fan, Ze Zhou Fan, Ji Tian

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.