Open Access
MATEC Web Conf.
Volume 139, 2017
2017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
Article Number 00010
Number of page(s) 7
Published online 05 December 2017
  1. Wu, Xiaokun, B. Cheng, and J. Chen. “Collaborative Filtering Service Recommendation Based on a Novel Similarity Computation Method.” IEEE Transactions on Services Computing 10, (2017), 352–365 [CrossRef] [Google Scholar]
  2. Chen. Hao, Z. Li, and W. Hu. An improved collaborative recommendation algorithm based on optimized user similarity. Journal of Supercomputing 72, (2015), 2565–2578 [CrossRef] [Google Scholar]
  3. Bobadilla, Jesús, F. Ortega, and A. Hernando. A collaborative filtering similarity measure based on singularities. Information Processing & Management 48, (2012), 204–217 [CrossRef] [Google Scholar]
  4. T. Huang, R. Huang. Improved Collaborative Filtering Recommendation Algorithm, Computer Science, 43, (2016), 400–403 [Google Scholar]
  5. BK. Patra, R. Launonen, V. Ollikainen, S. Nandi.A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data, Knowledge-Based Systems,82, (2015), 163–177 [Google Scholar]
  6. G. Guo,J. Zhang,N. Yorke-Smith.Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems, Knowledge-Based Systems, 74, (2015), 14–27 [Google Scholar]
  7. P. Chen, C. Chen. Incorporating Association Rules for Collaborative Filtering Recommendation Algorithm, Journal of Chinese Computer Systems, 37, (2016), 287–292 [Google Scholar]
  8. Peng Zhenlian, Wang Jian, He Keqing, Tang Mingdong. A Requirements Elicitation Approach Based on Feature Model and Collaborative Filtering. Journal of Computer Research and Development, 53, (2016), 2055–2066 [Google Scholar]
  9. GAO Quan-li, GAO Ling etc. Weighted Collaborative Filtering Algorithm with Fused Impact Facto [J]. Computer Engineering, 40, (2014), 39–41 [Google Scholar]
  10. LU Kun, XIE Ling, LI Ming-chu. Research on Implied-trust Aware Collaborative Filtering Recommendation Algorithm [J]. Journal of Chinese Computer Systems, 37, (2016), 241–243 [Google Scholar]
  11. GUO Yan-hong, DENG Gui-shi, LUO Chun-yu. Collaborative Filtering Recommendation Algorithm Based on Factor of Trust [J]. Computer Engineering, 34, (2008), 1–3 [Google Scholar]
  12. WANG Jing, YIN Jian etc. Collaborative Filtering Recommendation Algorithm Based on Co-ratings and Similarity Weight [J]. Computer Science, 37, (2010), 99–104 [Google Scholar]
  13. XU Xiang-yu, LIU Jian-ming. Collaborative Filtering Recommendation Algorithm Based on Multi-level Item Similarity [J]. Computer Science, 34, (2016), 262–265 [Google Scholar]
  14. XIA Pingping,SHUAI Jianmei. Collaborative Filtering Algorithm Based on Similarity Extension and Interest Degree Scaling [J]. Computer Engineering, 42, (2016), 199–202 [Google Scholar]
  15. WEN Jun-hao, SHU Shan. Improved Collaborative Filtering Recommendation Algorithm of Similarity Measure [J]. Computer Science, 41, (2014), 68–71 [Google Scholar]
  16. C. Ziegler, S.M. McNee, J.A. Konstan et al. Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web. Chiba: ACM, (2005), 22–32 [CrossRef] [Google Scholar]
  17. Ge M., Delgado-Battenfeld, Jannach D. Beyond accuracy: Evaluating recommender systems by coverage and serendipity. In:RecSys (2010): the 2010 ACM conference on Recommender systems. Barcelona:ACM,2010,257–260 [Google Scholar]
  18. Ge, Mouzhi, C. Delgado-Battenfeld, and D. Jannach. Beyond accuracy: evaluating recommender systems by coverage and serendipity. Recsys, (2010),257 [Google Scholar]

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