计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 521-525.doi: 10.11896/JsJkx.190900131
李建军, 付佳, 杨玉, 侯跃, 汪校铃, 荣欣
LI Jian-Jun, FU Jia, YANG Yu, HOU Yue, WANG Xiao-ling and RONG Xin
摘要: 当前互联网发展日益强大,农产品电商市场的竞争愈演愈烈,用户无法从众多的产品信息中找到适合自身的产品,传统的协同过滤算法只关注用户评分,并不能及时反映用户的兴趣变化。针对这一问题,文中主要考虑通过用户行为及用户访问时间和频率,提出基于改进权值的用户兴趣推荐算法(Weight-based User Interest-Collaborative Filtering,WUI-CF)。实验结果表明,所提算法相比于传统推荐算法能更好地挖掘用户兴趣,适应用户的兴趣变化,提高推荐的精确度,能够更好地解决用户面临众多农产品信息无从挑选的问题,提高了用户的满意度。
中图分类号:
[1] GOMBER P,KOCH J A,SIERING M.Digital Finance and FinTech:current research and future research directions.Journal of Business Economics,2017,87(5):537-580. [2] LENG Y J,LU Q,LIANG C Y.Collaborative filtering recommendation technology review .Pattern Recognition and Artificial Intelligence,2014(8):50-64. [3] MOU J J,LUO G K,XIONG Z B.Collaborative filtering algorithm applied to the recommendation of attractions .Software Guide,2017(11):186-188. [4] CHU X Q.Research on financial product recommendation based on collaborative filtering and immune-like algorithms .Nanchang:Nanchang University,2015. [5] XIONG H X.Research on User Clustering Recommendation Based on Label and Relational Network.DataAnalysis and Knowledge Discovery,2017,6(6):36-46. [6] LI L,TANG X J.Research on Cold Start Problem of Recommendation System Based on Decision Aggregation Model.Journal of Hubei University(Philosophy and Social Sciences),2016,43(2):41-44. [7] WANG S S,ZHAO H Y,CHEN Q K,et al.Probability Matrix Decomposition Recommendation Algorithm Based on Social Labeling and Social Trust.Small Computer Systems,2016,37(5):921-926. [8] GANDHI,MONALI.An Enhanced Approach towards Tourism Recommendation System with Hybrid Filtering and Association.National Journal of System and Information Technology,2015(8):1-8. [9] JIANG S H,QIAN X M,SHEN J L,et al.Travel Recommendation via Author Topic Model Based Collaborative Filtering.International Conference on Multimedia Modeling,2015:392-402. [10] XU B B,WANG W S,GUO L F.Application of Improved Collaborative Filtering Algorithm in Agricultural Materials E-commerce Website.Jiangsu Agricultural Sciences,2018,46(16):197-200. [11] YU M Y,ZHI H C.Design of a hybrid collaborative filtering method based on agricultural product recommendation.Automation Technology and Application,2017,36(2):82-84. [12] GUO W G.The Framework of Semantic Retrieval Recommendation System Based on Agricultural Ontology.Computer Knowledge and Technology,2019,15(17):191-193. [13] CHENG Y J.Research on hybrid recommendation algorithm for P2P online loan products .South China University of Technology,2016. [14] RONG H G,HUO S X,HU C H,et al.Collaborative Filtering Recommendation Algorithm Based on User Similarity.Journal on Communications,2016,35(2):18-19. [15] FAN B,CHENG J J.Multi-similarity collaborative filtering re-commendation algorithm between users.Computer Science,2012(1):23-26. [16] ZHANG Y L,XUN S S,LIANGS P.A Weighted Slope One Algorithm Combining User Similarity and ProJect Similarity.Miniature Microcomputer,2016(6):1176-1178. [17] XIAO Y H,WU M L.Application Research of Collaborative Filtering Algorithm for Improving User Similarity.Information Technology,2018(7):132-134. [18] WANG C C,XING H,LI Y T.Analysis of Recommended System Evaluation Methods and Indicators.Information Technology & Standardization,2015(7):28-29. [19] WANG C.Research on personalized recommendation algorithm based on user behavior .Harbin Institute of Technology,2015. [20] XU C Y.Research and System Implementation of Personalized Friends Recommendation Algorithm Based on Weibo Data.Shanxi University,2016. [21] LOPS P,DE G M,SEMERARO G,et al.Content-based and collaborative techniques for tag recommendation:an empirical eva-luation.Journal of Intelligent Information Systems,2013,40(1):41-45. |
[1] | 程章桃, 钟婷, 张晟铭, 周帆. 基于图学习的推荐系统研究综述 Survey of Recommender Systems Based on Graph Learning 计算机科学, 2022, 49(9): 1-13. https://doi.org/10.11896/jsjkx.210900072 |
[2] | 张佳, 董守斌. 基于评论方面级用户偏好迁移的跨领域推荐算法 Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer 计算机科学, 2022, 49(9): 41-47. https://doi.org/10.11896/jsjkx.220200131 |
[3] | 王冠宇, 钟婷, 冯宇, 周帆. 基于矢量量化编码的协同过滤推荐方法 Collaborative Filtering Recommendation Method Based on Vector Quantization Coding 计算机科学, 2022, 49(9): 48-54. https://doi.org/10.11896/jsjkx.210700109 |
[4] | 周芳泉, 成卫青. 基于全局增强图神经网络的序列推荐 Sequence Recommendation Based on Global Enhanced Graph Neural Network 计算机科学, 2022, 49(9): 55-63. https://doi.org/10.11896/jsjkx.210700085 |
[5] | 秦琪琦, 张月琴, 王润泽, 张泽华. 基于知识图谱的层次粒化推荐方法 Hierarchical Granulation Recommendation Method Based on Knowledge Graph 计算机科学, 2022, 49(8): 64-69. https://doi.org/10.11896/jsjkx.210600111 |
[6] | 方义秋, 张震坤, 葛君伟. 基于自注意力机制和迁移学习的跨领域推荐算法 Cross-domain Recommendation Algorithm Based on Self-attention Mechanism and Transfer Learning 计算机科学, 2022, 49(8): 70-77. https://doi.org/10.11896/jsjkx.210600011 |
[7] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
[8] | 帅剑波, 王金策, 黄飞虎, 彭舰. 基于神经架构搜索的点击率预测模型 Click-Through Rate Prediction Model Based on Neural Architecture Search 计算机科学, 2022, 49(7): 10-17. https://doi.org/10.11896/jsjkx.210600009 |
[9] | 齐秀秀, 王佳昊, 李文雄, 周帆. 基于概率元学习的矩阵补全预测融合算法 Fusion Algorithm for Matrix Completion Prediction Based on Probabilistic Meta-learning 计算机科学, 2022, 49(7): 18-24. https://doi.org/10.11896/jsjkx.210600126 |
[10] | 蔡晓娟, 谭文安. 一种改进的融合相似度和信任度的协同过滤算法 Improved Collaborative Filtering Algorithm Combining Similarity and Trust 计算机科学, 2022, 49(6A): 238-241. https://doi.org/10.11896/jsjkx.210400088 |
[11] | 王毅, 李政浩, 陈星. 基于用户场景的Android 应用服务推荐方法 Recommendation of Android Application Services via User Scenarios 计算机科学, 2022, 49(6A): 267-271. https://doi.org/10.11896/jsjkx.210700123 |
[12] | 何亦琛, 毛宜军, 谢贤芬, 古万荣. 基于点割集图分割的矩阵变换与分解的推荐算法 Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation 计算机科学, 2022, 49(6A): 272-279. https://doi.org/10.11896/jsjkx.210600159 |
[13] | 李小伟, 舒辉, 光焱, 翟懿, 杨资集. 自然语言处理在简历分析中的应用研究综述 Survey of the Application of Natural Language Processing for Resume Analysis 计算机科学, 2022, 49(6A): 66-73. https://doi.org/10.11896/jsjkx.210600134 |
[14] | 朴勇, 朱锶源, 李阳. 融合用户和区位资源特征的混合房源推荐方法 Hybrid Housing Resource Recommendation Based on Combined User and Location Characteristics 计算机科学, 2022, 49(6A): 733-737. https://doi.org/10.11896/jsjkx.210800062 |
[15] | 蒲岍岍, 雷航, 李贞昊, 李晓瑜. 增强列表信息和用户兴趣的个性化新闻推荐算法 Personalized News Recommendation Algorithm with Enhanced List Information and User Interests 计算机科学, 2022, 49(6): 142-148. https://doi.org/10.11896/jsjkx.210400173 |
|