Adaptive Recommendation Model Based on User Behaviors
Xiang Zhuoyuan1(),Liu Zhicong2,Wu Yu1
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China 2Data Center of China Construction Bank, Wuhan 430073, China
[Objective] This paper proposes an adaptive recommendation model based on user’s behaviors, aiming to address the issues of one model only working for one user type. [Methods] We standardized the recommendation process with a three-tier collaborative structure. The first layer classified users to create different recommendation channels. The second layer matched the improved recommendation sub-algorithm with corresponding channels. The third layer introduced feature weighting to form a recommendation pool, from which the items were selected and recommended to users. [Results] The accuracy, recall, coverage and popularity of the proposed model were 0.24, 0.17, 0.50 and 4.40, which were better than the mainstream models. [Limitations] Our recommendation algorithm cannot work on datasets without scores. [Conclusions] The proposed model can learn the preferences of users and make better recommendations.
向卓元,刘志聪,吴玉. 基于用户行为自适应推荐模型研究 *[J]. 数据分析与知识发现, 2021, 5(4): 103-114.
Xiang Zhuoyuan,Liu Zhicong,Wu Yu. Adaptive Recommendation Model Based on User Behaviors. Data Analysis and Knowledge Discovery, 2021, 5(4): 103-114.
Persson P. Attention Manipulation and Information Overload[J]. Behavioural Public Policy, 2018,2(1):78-106.
doi: 10.1017/bpp.2017.10
[2]
Aswani R, Kar A K, Ilavarasan P V, et al. Search Engine Marketing is Not All Gold: Insights from Twitter and SEOClerks[J]. International Journal of Information Management, 2018,38(1):107-116.
doi: 10.1016/j.ijinfomgt.2017.07.005
[3]
Lu J, Wu D, Mao M, et al. Recommender System Application Developments: A Survey[J]. Decision Support Systems, 2015,74:12-32.
doi: 10.1016/j.dss.2015.03.008
[4]
Lika B, Kolomvatsos K, Hadjiefthymiades S. Facing the Cold Start Problem in Recommender Systems[J]. Expert Systems with Applications, 2014,41(4):2065-2073.
doi: 10.1016/j.eswa.2013.09.005
[5]
Feng J, Fengs X, Zhang N, et al. An Improved Collaborative Filtering Method Based on Similarity[J]. PLoS ONE, 2018,13(9):e0204003.
doi: 10.1371/journal.pone.0204003
[6]
Jamali M, Ester M. TrustWalker: A Random Walk Model for Combining Trust-Based and Item-Based Recommendation[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2009: 397-406.
[7]
Lu J, Shambour Q, Xu Y, et al. A Web-Based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques[J]. Computational Intelligence, 2013,29(1):37-69.
doi: 10.1111/coin.2013.29.issue-1
[8]
Bobadilla J, Ortega F, Hernando A, et al. A Collaborative Filtering Approach to Mitigate the New User Cold Start Problem[J]. Knowledge-Based Systems, 2012,26:225-238.
doi: 10.1016/j.knosys.2011.07.021
[9]
Ahn H J. A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-Starting Problem[J]. Information Sciences, 2008,178(1):37-51.
doi: 10.1016/j.ins.2007.07.024
[10]
Liu H, Hu Z, Mian A, et al. A New User Similarity Model to Improve the Accuracy of Collaborative Filtering[J]. Knowledge-Based Systems, 2014,56:156-166.
doi: 10.1016/j.knosys.2013.11.006
[11]
Son L H. HU-FCF: A Hybrid User-Based Fuzzy Collaborative Filtering Method in Recommender Systems[J]. Expert Systems with Applications: An International Journal, 2014,41(15):6861-6870.
doi: 10.1016/j.eswa.2014.05.001
[12]
Karahodza B, Donko D. Feature Enhanced Time-Aware Recommender System[C]//Proceedings of the 25th International Conference on Information, Communication and Automation Technologies. IEEE, 2015: 1-6.
[13]
Xia C, Jiang X, Liu S, et al. Dynamic Item-Based Recommendation Algorithm with Time Decay[C]//Proceedings of the 6th International Conference on Natural Computation. IEEE, 2010,1:242-247.
[14]
Karahodza B, Supic H, Donko D. An Approach to Design of Time-Aware Recommender System Based on Changes in Group User's Preferences[C]//Proceedings of the 10th International Symposium on Telecommunications. IEEE, 2014: 1-4.
( Lan Yan, Cao Fangfang. Research of Time Weighted Collaborative Filtering Algorithm for Movie Recommendation[J]. Computer Science, 2017,44(4):295-301, 322.)
[16]
Xing S, Liu F, Wang Q, et al. A Hierarchical Attention Model for Rating Prediction by Leveraging User and Product Reviews[J]. Neurocomputing, 2019,332:417-427.
doi: 10.1016/j.neucom.2018.12.027
[17]
Wang J, Lü J. Tag-Informed Collaborative Topic Modeling for Cross Domain Recommendations[J]. Knowledge-Based Systems, 2020,203:106119.
doi: 10.1016/j.knosys.2020.106119
[18]
Kala A, Zayani C, Amous I, et al. Social Collaborative Service Recommendation Approach Based on User’s Trust and Domain-Specific Expertise[J]. Future Generation Computer Systems, 2018,80:355-367.
doi: 10.1016/j.future.2017.05.036
( Luo Haiyuan, Zhang Mu. Collaborative Filtering Algorithm Based on Users’ Multi-attribute Weighting and Interests Similarity[J]. Information Research, 2018(5):1-7.)
( Fu Fen, Dou Yusheng, Han Peng, et al. Learning Resource Recommendation Based on Implicit Scoring and Similarity Propagation[J]. Application Research of Computers, 2017,34(12):3725-3729.)
( Xu Yuanping, Chen Xiang. Research on Novelty Problems in Recommendation Systems[J]. Application Research of Computers, 2020,37(8):2310-2314.)
[22]
Jamali M, Ester M. A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks[C]// Proceedings of the ACM Conference on Recommender Systems. 2010.