Collaborative Filtering Recommendation Based on Trust Model with Fused Similar Factor

Li Ye; Chunming Wu; Min Li

doi:10.1051/matecconf/201713900010

All issues

Volume 139 (2017)

MATEC Web Conf., 139 (2017) 00010

References

Open Access

Issue		MATEC Web Conf. Volume 139, 2017 2017 3^rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)


Article Number		00010
Number of page(s)		7
DOI		https://doi.org/10.1051/matecconf/201713900010
Published online		05 December 2017

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]
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]
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]
T. Huang, R. Huang. Improved Collaborative Filtering Recommendation Algorithm, Computer Science, 43, (2016), 400–403 [Google Scholar]
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]
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]
P. Chen, C. Chen. Incorporating Association Rules for Collaborative Filtering Recommendation Algorithm, Journal of Chinese Computer Systems, 37, (2016), 287–292 [Google Scholar]
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]
GAO Quan-li, GAO Ling etc. Weighted Collaborative Filtering Algorithm with Fused Impact Facto [J]. Computer Engineering, 40, (2014), 39–41 [Google Scholar]
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]
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]
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]
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]
XIA Pingping,SHUAI Jianmei. Collaborative Filtering Algorithm Based on Similarity Extension and Interest Degree Scaling [J]. Computer Engineering, 42, (2016), 199–202 [Google Scholar]
WEN Jun-hao, SHU Shan. Improved Collaborative Filtering Recommendation Algorithm of Similarity Measure [J]. Computer Science, 41, (2014), 68–71 [Google Scholar]
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]
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]
Ge, Mouzhi, C. Delgado-Battenfeld, and D. Jannach. Beyond accuracy: evaluating recommender systems by coverage and serendipity. Recsys, (2010),257 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[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]