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User Group-based Method for Cold-Start Recommendation

Volume 14, Number 8, August 2018, pp. 1719-1725
DOI: 10.23940/ijpe.18.08.p8.17191725

Jing He, Shuo Yuan, Yi Xiang, and Wei Zhou

National Pilot School of Software, Yunnan University, Kunming, 650091, China

(Submitted on April 29, 2018; Revised on June 15, 2018; Accepted on July 14, 2018)


Recommendation algorithms seek to predict user ratings or preferences. Due to limited information, it is difficult to make these predictions for new users. Therefore, a dynamic cold-start recommendation algorithm would be highly helpful in such quick-changing social networks. In this paper, a novel user group-based collaborative method, called UCFRA (User group-based Cold-start Friend Recommendation Algorithm) is proposed. UCFRA integrates a graphical model and statistical population characteristics into a user group model and then combines this extended user group model with cold-start information to generate a new recommendation algorithm. Moreover, a content popularity model based on user groups and a user rating matrix is designed. In order to improve recommend precision, a user group Top-N recommendation model based on k-nearest neighbors is provided. A series of experiments involving collection of a huge data set was developed to evaluate the effectiveness of UCFRA. The experimental results showed that UCFRA is a valid algorithm.


References: 21

              1. M. An, “The Future of Content Marketing: How People are Changing the Way They Read, Interact, and Engage with Content,” (, accessed June 25, 2016)
              2. T. A. Pempek, Y. A. Yermolayeva, and S. L. Calvert, “College Students’ Social Networking Experiences on Facebook,” Journal of Applied Developmental Psychology, Vol. 30, No. 3, pp. 227-238, 2009
              3. M. Moricz, Y. Dosbayev, and M. Berlyant, “PYMK: Friend Recommendation at Myspace,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, pp.999-1002, New York, 2010
              4. M. Ye, P. Yin, and W. C. Lee, “Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation,” in Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 325-334, 2011
              5. S. Atisha and R. Vineet, “A Survey on Recommender Systems based on Collaborative Filtering Technique,” International Journal of Innovations in Engineering and Technology, Vol. 2, No. 2, pp. 8-14, 2013
              6. Z. Yu, C. Wang, and J. Bu, “Friend Recommendation with Content Spread Enhancement in Social Networks,” Information Sciences, No. 309, pp.102-118, 2015
              7. S. Huang, J. Zhang, and L. Wang. “Social Friend Recommendation based on Multiple Network Correlation,” IEEE transactions on multimedia, Vol. 18, No. 2, pp. 287-299, 2016
              8. C. He, D. Parra, and K. Verbert, “Interactive Recommender Systems: A Survey of the State of the Art and Future Research Challenges and Opportunities,” Expert Systems with Applications, No. 56, pp. 9-27, 2016
              9. P. Jyoti, J. Maitri, K. Abbas, and T. Malhar, “Recommendation System using Social Networking,” International Journal of Computer Science, Engineering and Information Technology, Vol. 2, No. 5, pp. 45-54, 2012
              10. M. Saveski and A. Mantrach, “Item Cold-Start Recommendations: Learning Local Collective Embedding,” in Proceedings of the 8th ACM Conference on Recommender Systems, pp. 89-96, 2014
              11. U. Ocepek, J. Rugelj, and Z. Bosnić, “Improving Matrix Factorization Recommendations for Examples in Cold Start,” Expert Systems with Applications, Vol. 42, No. 19, pp. 6784-6794, 2015
              12. F. Peng, X. Lu, and C. Ma, “Multi-Level Preference Regression for Cold-Start Recommendations,” International Journal of Machine Learning and Cybernetics, pp. 1-14, 2017
              13. Y. J. Yang, H. Z. Zhang, and X. F. Wang, “On Alleviation of New User Problem in Collaborative Filtering using SNA Theory,” International Journal of u- and e- Service, Science and Technology, Vol. 6, No. 6, pp. 121-132, 2013
              14. J. Lin, K. Sugiyama, M.Y. Kan, and T. S. Chua, “Addressing Cold-Start in App Recommendation: Latent User Models Constructed from Twitter Followers,” in Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 283-292, 2013
              15. M. Daoud, S. K. Naqvi, and T. Siddiqi, “An Item-Oriented Algorithm on Cold-start Problem in Recommendation System,” International Journal of Computer Applications, Vol. 116, No. 11, pp. 19-24, 2015
              16. C. Huang and J. Yin, “Effective Association Clusters Filtering to Cold-Start Recommendations,” in Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 2461-2464, 2010
              17. M. K. Najafabadi, M. N. Mahrin, and S. Chuprat, “Improving the Accuracy of Collaborative Filtering Recommendations using Clustering and Association Rules Mining on Implicit Data,” Computers in Human Behavior, No. 67, pp. 113-128, 2017
              18. S. Gupta and S. Goel, “Handling User Cold Start Problem in Recommender Systems using Fuzzy Clustering,” Information and Communication Technology for Sustainable Development, Springer, Singapore, pp.143-151, 2018
              19. D. G. Ferrari and L. N. De Castro, “Clustering Algorithm Selection by Meta-Learning Systems: A New Distance-based Problem Characterization and Ranking Combination Methods,” Information Sciences, No. 301, pp. 181-194, 2015
              20. J. Wei, J. He, and K. Chen, “Collaborative Filtering and Deep Learning based Recommendation System for Cold Start Items,” Expert Systems with Applications, No. 69, pp. 29-39, 2017
              21. G. Roumelis, M. Vassilakopoulos, and A. Corral, “Efficient Query Processing on Large Spatial Databases: A Performance Study,” Journal of Systems and Software, No. 132, pp. 165-185, 2017


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