| Citation: | Ming ZHAO, Han YAN, Gaofeng CAO, Xinhong LIU. Robust Recommendation Algorithm Based on Core User Extraction with User Trust and Similarity[J]. Journal of Electronics & Information Technology, 2019, 41(1): 180-186. doi: 10.11999/JEIT180142
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Recommendation systems can help people make decisions conveniently. However, few studies consider the effect of removing irrelevant noise users and retaining a small number of core users to make recommendations. A new method of core user extraction is proposed based on trust relationship and interest similarity. First, all users trust and interest similarity between pairs are calculated and sorted, then according to the frequency and position weight users travel in the nearest neighbor in the list of two kinds of strategies for the selection of candidate core collection of users. Finally, according to the user’s ability the core users are sieved out. Experimental results show that the core user recommendation effectiveness, and verify that the core of user 20% can reach more than recommended accuracy of 90%, and through the use of core user recommendation the negative effects caused by the attacks on the recommendation system can be resisted.
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TAO Peng, WANG Wendong, GONG Xiangyang, et al. A graph indexing approach for content-based recommendation system[C]. IEEE International Conference on Multimedia & Information Technology, Kaifeng China, 2010: 93–97.
|
|
王玉斌, 孟祥武, 胡勋. 一种基于信息老化的协同过滤推荐算法[J]. 电子与信息学报, 2013, 35(10): 2391–2396. doi: 10.3724/SP.J.1146.2012.01743
WANG Yubin, MENG Xiangwu, and HU Xun. A collaborative filtering recommendation algorithm based on information aging[J]. Journal of Electronics &Information Technology, 2013, 35(10): 2391–2396. doi: 10.3724/SP.J.1146.2012.01743
|
|
王海艳, 张大印. 一种可信的基于协同过滤的服务选择模型[J]. 电子与信息学报, 2013, 35(2): 349–354. doi: 10.3724/SP.J.1146.2012.00946
WANG Haiyan and ZHANG Dayin. A trusted service selection model based on collaborative filtering[J]. Journal of Electronics &Information Technology, 2013, 35(2): 349–354. doi: 10.3724/SP.J.1146.2012.00946
|
|
ADOMAVICIUS G and TUZHILIN A. Context-Aware Recommender Systems[M]. USA: Springer, 2015: 2175–2178.
|
|
PANNIELLO U, TUZHILIN A, and GORGOGLIONE M. Comparing context-aware recommender systems in terms of accuracy and diversity[J]. User Modeling and User-Adapted Interaction, 2014, 24(1/2): 35–65. doi: 10.1007/s11257-012-9135-y
|
|
ZENG Wei, ZENG An, LIU Hao, et al. Uncovering the information core in recommender systems[J]. Scientific Reports, 2014, 4: 6140–6148. doi: 10.1038/srep06140
|
|
徐风苓, 孟祥武, 王立才. 基于移动用户上下文相似度的协同过滤推荐算法[J]. 电子与信息学报, 2011, 33(11): 2785–2789. doi: 10.3724/SP.J.1146.2011.00384
XU Fengling, MENG Xiangwu, and WANG Licai. Collaborative filtering recommendation algorithm based on context similarity of mobile users[J]. Journal of Electronics &Information Technology, 2011, 33(11): 2785–2789. doi: 10.3724/SP.J.1146.2011.00384
|
|
LU Linyuan and LIU Weiping. Information filtering via preferential diffusion[J]. Physical Review E, 2011, 83(6): 066119. doi: 10.1103/PhysRevE.83.066119
|
|
ZHOU Tao, ZOLTÁN K, LIU Jianguo, et al. Solving the apparent diversity-accuracy dilemma of recommender systems[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(10): 4511–4515. doi: 10.1073/pnas.1000488107
|
|
XU Fuqing, WANG Zhiwu, TANG Li, et al. A mass diffusion-based interpretation of the effect of total solids content on solid-state anaerobic digestion of cellulosic biomass[J]. Bioresource Technology, 2014, 167(3): 178–185. doi: 10.1016/j.biortech.2014.05.114
|
|
黄武汉, 孟祥武, 王立才. 移动通信网中基于用户社会化关系挖掘的协同过滤算法[J]. 电子与信息学报, 2011, 33(12): 3002–3007. doi: 10.3724/SP.J.1146.2011.00364
HUANG Wuhan, MENG Xiangwu, and WANG Licai. Collaborative filtering algorithm based on user social relationship mining in mobile communication network[J]. Journal of Electronics &Information Technology, 2011, 33(12): 3002–3007. doi: 10.3724/SP.J.1146.2011.00364
|
|
CHANG Na, IRVAN M, and TERANO T. An item influence-centric algorithm for tecommender dystems[C]. 11th International Conference on Distributed Computing and Artificial Intelligence, Salamanca, Spain, 2014: 553–560.
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