Computer Science ›› 2019, Vol. 46 ›› Issue (2): 210-214.doi: 10.11896/j.issn.1002-137X.2019.02.032
• Artificial Intelligence • Previous Articles Next Articles
WANG Yong1, WANG Yong-dong1, DENG Jiang-zhou1, ZHANG Pu2
CLC Number:
| [1]CHOU A Y.The analysis of online social networking:How technology is changing e-commerce purchasing decision[J].International Journal of Information Systems & Change Management,2010,4(4):353-365. [2]YANG C C.Correlation coefficient evaluation for the fuzzy interval data[J].Journal of Business Research,2016,69(6):2138-2144. [3]GUAN H,GUAN S,ZHAO A.Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity[J].Symmetry,2017,9(9):191. [4]TAKACS G,PILASZY I,NEMETH B,et al.Scalable Collaborative Filtering Approaches for Large Recommender System [J].Journal of Machine Learning Research,2009,10:623-656. [5]KIM H N,JI A T,HA I,et al.Collaborative Filtering Based on Collaborative Tagging for Enhancing the Quality of Recommendation [J].Electronic Commerce Research and Applications,2010,9(1):73-83. [6]KHROUF H.Hybrid event recommendation using linked data and user diversity[C]∥ ACM Conference on Recommender Systems.ACM,2013:185-192. [7]MEYMANDPOUR R,DAVIS J G.Recommendations using linked data[C]∥ Proceedings of the 5th Ph.d.Workshop on Information and Knowledge.ACM,2012:75-82. [8]OSTUNI V C,NOIA T D,SCIASCIO E D,et al.Top-N recom- mendations from implicit feedback leveraging linked open data[C]∥ACM Conference on Recommender Systems.ACM,2013:85-92. [9]BARJASTEH I,FORSATI R,MASROUR F,et al.Cold-Start Item and User Recommendation with Decoupled Completion and Transduction[C]∥ ACM Conference on Recommender Systems.ACM,2015:91-98. [10]BINESH N,REZGHI M.A new similarity measure for extraction information from social networks and improve the community detection and recommendation results[C]∥Information and Knowledge Technology.IEEE,2015:146-151. [11]WANG X M,ZHANG X M,WU Y T,et al.A Collaborative Recommendation Algorithm Based on Heuristic Clustering Modeland Category Similarity[J].Acta Electronica Sinica,2016,44(7):1708-1713.(in Chinese) 王兴茂,张兴明,吴毅涛,等.基于启发式聚类模型和类别相似度的协同过滤推荐算法[J].电子学报,2016,44(7):1708-1713. [12]WANG Y,DENG J Z,DENG Y H,et al.A Collaborative Filtering Recommendation Algorithm Based on Item Probability Distribution[J].New Technology of Library and Information Ser-vice,2016,32(6):73-79.(in Chinese) 王永,邓江洲,邓永恒,等.基于项目概率分布的协同过滤推荐算法[J].现代图书情报技术,2016,32(6):73-79. [13]PATRA B K,LAUNONEN R,OLLIKAINEN V,et al.A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data[J].Knowledge-Based Systems,2015,82(3):163-177. [14]MANNING C D.Foundations of statistical natural language processing[M].Massachusetts:MIT Press,1999. [15]MAJTEY A P,LAMBERTI P W,PRATO D P.Jensen-Shannon divergence as a measure of distinguishability between mixed quantum states[J].Physical Review A,2005,72(5):762-776. [16]WILLMOTT C J,MATSUURA K.Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error(RMSE) in Assessing Average Model Performance[J].Climate Research,2005,30(1):79-82. |
| [1] | CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning [J]. Computer Science, 2022, 49(9): 1-13. |
| [2] | WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54. |
| [3] | SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56. |
| [4] | CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241. |
| [5] | HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279. |
| [6] | GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao. Hybrid Recommender System Based on Attention Mechanisms and Gating Network [J]. Computer Science, 2022, 49(6): 158-164. |
| [7] | WANG Mei-ling, LIU Xiao-nan, YIN Mei-juan, QIAO Meng, JING Li-na. Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions [J]. Computer Science, 2022, 49(3): 99-104. |
| [8] | DONG Xiao-mei, WANG Rui, ZOU Xin-kai. Survey on Privacy Protection Solutions for Recommended Applications [J]. Computer Science, 2021, 48(9): 21-35. |
| [9] | ZHAN Wan-jiang, HONG Zhi-lin, FANG Lu-ping, WU Zhe-fu, LYU Yue-hua. Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning [J]. Computer Science, 2021, 48(7): 172-177. |
| [10] | SHAO Chao, SONG Shu-mi. Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship [J]. Computer Science, 2021, 48(6A): 240-245. |
| [11] | HUANG Ming, SUN Lin-fu, REN Chun-hua , WU Qi-shi. Improved KNN Time Series Analysis Method [J]. Computer Science, 2021, 48(6): 71-78. |
| [12] | WU Jian-xin, ZHANG Zhi-hong. Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest [J]. Computer Science, 2021, 48(5): 147-154. |
| [13] | ZHANG Yan-jin, BAI Liang. Fast Symbolic Data Clustering Algorithm Based on Symbolic Relation Graph [J]. Computer Science, 2021, 48(4): 111-116. |
| [14] | XIAO Shi-tao, SHAO Ying-xia, SONG Wei-ping, CUI Bin. Hybrid Score Function for Collaborative Filtering Recommendation [J]. Computer Science, 2021, 48(3): 113-118. |
| [15] | WANG Xing , KANG Zhao. Smooth Representation-based Semi-supervised Classification [J]. Computer Science, 2021, 48(3): 124-129. |
|
||
Home