Friend Recommendation Feedback Algorithm Combining Cognition and Interest
YIN Yunfei1,2, SUN Jingqin1, HUANG Faliang3, BAI Xiangyu1
1. College of Computer Science, Chongqing University, Chong-qing 400044 2. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing 400044 3. School of Computer and Information Engineering, Nanning Normal University, Nanning 530001
Abstract:In the existing friend recommendation algorithm, important information is lost in the portrayal of the friend relationship. Inspired by the user's cognitive behavior of the item, a friend recommendation feedback algorithm based on cognition and interest is proposed in this paper. Hybrid similarity is utilized to conduct online friend relationship research and explore friendship issues in online social networks. Aiming at the open loop problem of the friend recommendation process, a positive and negative feedback optimization adjustment strategy based on historical recommendation information is proposed. The user similarity correction formula is employed for friend feedback dynamic recommendation, and it is proved that friend recommendation is a complex process of gradual correction. The psychological and cognitive problems portrayed by friend relationships in online social networks and the dynamic changes of recommendations are presented. The experiments show that the proposed algorithm improves the recommendation quality and realizes the dynamic adjustment of the user similarity matrix and it is superior in accuracy, recall, robustness and scalability.
[1] BIGORRA A M, ISAKSSON O, KARLBERG M. Semi-autonomous Methodology to Validate and Update Customer Needs Database through Text Data Analytics. International Journal of Information Ma-nagement, 2020, 52(C). DOI: 10.1016/j.ijinfomgt.2020.102073. [2] WANG X F, WANG C Y, CHEN X, et al. Measurement and Ana-lysis on Large-Scale Offline Mobile APP Dissemination over Device-to-Device Sharing in Mobile Social Networks. World Wide Web, 2020, 23(4): 2363-2389. [3] 孟祥武,刘树栋,张玉洁,等.社会化推荐系统研究.软件学报, 2015, 26(6): 1356-1372. (MENG X W, LIU S D, ZHANG Y J, et al. Research on Social Recommender Systems. Journal of Software, 2015, 26(6): 1356-1372.) [4] CHEN J L, GEYER W, DUGAN C, et al. Make New Friends, But Keep the Old: Recommending People on Social Networking Sites // Proc of the SIGCHI Conference on Human Factors in Computing Systems. New York, USA: ACM, 2009: 201-210. [5] 李 慧,马小平,施 珺,等.复杂网络环境下基于信任传递的推荐模型研究.自动化学报, 2018, 44(2): 363-376. (LI H, MA X P, SHI J, et al. A Recommendation Model by Mean of Trust Transition in Complex Network environment. Acta Automatica Sinica, 2018, 44(2): 363-376.) [6] JI Z Y, YANG C, WANG H H, et al. BRScS: A Hybrid Reco-mmendation Model Fusing Multi-source Heterogeneous Data. EURASIP Journal on Wireless Communications and Networking, 2020(1). DOI: 10.1186/s13638-020-01716-2. [7] YANG C F, ZHOU Y P, CHIU D M. Who Are Like-Minded: Mining User Interest Similarity in Online Social Networks[C/OL]. [2020-09-24]. https://arxiv.org/pdf/1603.02175.pdf. [8] CHEN H H, JIN H, CUI X L. Hybrid Followee Recommendation in Microblogging Systems. Science China(Information Sciences), 2017, 60: 21-34. [9] HU Y, PENG Q M, HU X H, et al. Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering. IEEE Transactions on Services Computing, 2015, 8(5): 782-794. [10] CHEN C C, ZHENG X L, WANG Y, et al. Capturing Semantic Correlation for Item Recommendation in Tagging Systems // Proc of the 30th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2016: 108-114. [11] ZHOU T C, MA H, LYU M R, et al. UserRec: A User Reco-mmendation Framework in Social Tagging Systems // Proc of the 24th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2010: 1486-1491. [12] JAIN A, NAGAR S, SINGH P K, et al. EMUCF: Enhanced Multistage User-Based Collaborative Filtering through Non-linear Similarity for Recommendation Systems. Expert Systems with Applications, 2020, 161. DOI: 10.1016/j.eswa.2020.113724. [13] CHANG Y Q, SHI Y M, WANG Y W, et al. Bi-directional Re-ranking for Person Re-identification // Proc of the IEEE Confe-rence on Multimedia Information Processing and Retrieval. Wa-shington, USA: IEEE, 2019: 48-53. [14] SHANG M S, ZHANG Z K, ZHOU T, et al. Collaborative Fil-tering with Diffusion-Based Similarity on Tripartite Graphs. Physica A(Statistical Mechanics and Its Applications), 2010, 389(6): 1259-1264. [15] 陈洁敏,李建国,汤非易,等.融合"用户-项目-用户兴趣标签图"的协同好友推荐算法.计算机科学与探索, 2018, 12(1): 92-100. (CHEN J M, LI J G, TANG F Y, et al. Combining User-Item-Tag Tripartite Graph and Users' Personal Interests for Friends Reco-mmendation. Journal of Frontiers of Computer Science and Technology, 2018, 12(1): 92-100.) [16] DAVIS N, HSIAO C P, SINGH Y K, et al. Empirically Studying Participatory Sense-Making in Abstract Drawing with a Co-creative Cognitive Agent // Proc of the 21st International Conference on Intelligent User Interfaces. New York, USA: ACM, 2016: 196-207. [17] KIEFER P, GIANNOPOULOS I, RAUBAL M, et al. Eye Trac-king for Spatial Research: Cognition, Computation, Challenges. Spatial Cognition and Computation, 2017, 17(1/2): 1-19. [18] PAUL A, DANIEL A, AHMAD A, et al. Cooperative Cognitive Intelligence for Internet of Vehicles. IEEE Systems Journal, 2015, 11(3): 1249-1258.