曾偉 電子科技大學副教授。一共發表學術論文14篇，以第一作者發表學術論文10篇，其中6篇SCI檢索論文，論文發表期刊影響因子最高5.578，總的影響因子20.72。長期從事推薦系統的研究工作，研究內容包括：推薦系統多樣性、數據稀疏性和推薦網路結構分析。擅長於算法設計、數值分析和建模。多次到香港、海外知名高校訪問，例如香港浸會大學、華沙大學、瑞士弗里堡大學、羅馬大學。一共主持了兩項國際項目和一項國家自然科學基金項目：On the diversity Problem of Recommender Systems (EG57-092011)、Sino Swiss Science and Technology Cooperation Program Follow-up Grants (TE-70382)和推薦系統的信息核挖掘及其套用研究(61502078)，作為主研參與了多項國家科學自然基金項目。目前從事的工作主要包括：個性化推薦、國家GDP預測、大數據金融。金融大數據的研究內容包括金融產品個性化行銷和金融風險預測預警，目前已服務於中國銀行、建設銀行、民生銀行和成都銀行等多家金融機構。
論文列表
研究項目
1. On the diversity Problem of Recommender Systems （EG57-092011），瑞士聯邦政，2012 –2013。
2.Sino Swiss Science and Technology Cooperation Program Follow-up Grants（TE-70382），瑞士聯邦政府，2014. 7 – 2014. 9。
3. 推薦系統的信息核挖掘及其套用研究（61502078），國家科學自然基金，2016-2018。
4. 大數據金融，民生銀行，主持，2014-2015
5. 個性化行銷，中行四川省分行，主持，2016-2017
6. 風險預警，成都銀行，主持，2015-2016
[1] Wei Zeng, An Zeng, Hao Liu, Ming-sheng Shang and Tao Zhou, Uncovering the information core in recommender systems, Scientific Report 4: 6140. (SCI, IF=5.578, AN6OO)
[2] Wei Zeng, An Zeng, Ming-sheng Shang, Yi-cheng Zhang, Information filtering in sparse online systems: recommendation via semi-local diffusion, PLoS ONE 8: e79354. (SCI, IF=3.52, 256KH)
[3] Wei Zeng, An Zeng, Hao Liu, Ming-sheng Shang and Yi-cheng Zhang, Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering, PLoS ONE. (SCI, IF=3.52)
[4] Wei Zeng, Yuxiao Zhu, Linyuan Lü and Tao Zhou, Negative ratings play a positive role in information filtering, Physica A,390: 4486-4493 (SCI, IF=1.722, 829RP)
[5] Wei Zeng, An Zeng, Ming-sheng Shang and Yi-cheng Zhang, Membership in social networks and the application in information filtering, The European Physical Journal B, 86: 375. (SCI, IF=1.483, 226EJ)
[6] Wei Zeng, Ming-Sheng Shang, Qian-Ming Zhang, LinYuan Lü, Tao Zhou, Can dissimilar users contribute to accuracy and diversity of personalized recommendation? International Journal of Modern Physics C (IJMPC), 2010, 21: 1217-1227（SCI, IF=1.125）
[7] Wei Zeng and Li Chen, Heterogeneous Data Fusion via Matrix Factorization for Augmenting Item, Group and Friend Recommendations, The 28th ACM Symposium On Applied Computing, SAC 2013, Coimbra, Portugal, March 18 - 22, 2013. （EI檢索）
[8] Wei Zeng and Li Chen. Recommending Interest Groups to Social Media Users by Incorporating Heterogeneous Resources. In Proceedings of 26th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE’13), pages 361-371, Amsterdam, Netherlands, June 17-21, 2013. (EI檢索)
[9] Li Chen and Wei Zeng and Quan Yuan, A unified framework for recommending items, groups and friends in social media environment via mutual resource fusion, Expert Syst. Appl. 40: 2889-2903. (SCI, IF=1.965, 111LN)
[10] Ming-Sheng Shang, LinYuan Lü, Wei Zeng, Tao Zhou and Yi-Cheng Zhang, Relevance is more significant than correlation: Information filtering on sparse data. Europhysics Letters, 2009, 88:68008（SCI, IF=2.269）
[11] Wei Zeng, Ming-Sheng Shang, Tie-Yun Qian, Useful acquiring ratings for collaborative filtering. 2009 IEEE Youth Conference on Information Computing and Telecommunication (YC-ICT '09), 483.（EI檢索）
[12] Wei Zeng and Ming-Sheng Shang, Effects of negative ratings on personalized recommendation. 2010 5th International Conference on Computer Science and Education (ICCSE), 375（EI檢索）
[13] Yu-Xiao Zhu, Wei Zeng and Qian-Ming Zhang, The effect of rating variance on personalized recommendation, 2010 5th International Conference on Computer Science and Education (ICCSE), 366（EI檢索）
[14] Qian-Ming Zhang, Ming-Sheng Shang, Wei Zeng, Yong Chen and Linyuan Lü, Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems, Physics Procedia, 2010, 3: 1887-1896（EI檢索）
