计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 232-235.doi: 10.11896/jsjkx.201200010
朱育颉1, 刘虎沉2
ZHU Yu-jie1, LIU Hu-chen2
摘要： 推荐系统能帮助用户有效解决信息过载问题,现已被广泛应用于各大网上的购物平台。对用户而言,好的推荐算法能够帮助其从海量商品中快速准确发现符合自己需求的商品;对商家而言,及时呈现给用户恰当的物品能帮助商家实现精准营销,发掘长尾商品并推荐给感兴趣的用户以提高销售额。协同过滤、基于内容推荐是目前应用成熟的推荐方法,但这些方法存在数据疏散、冷启动、可扩展性差和多媒体信息特征难以提取等问题。因此,文中提出基于融合LR-GBDT-XGBOOST的个性化推荐算法,可有效缓解上述问题。在阿里巴巴天池大数据竞赛公开数据集上进行实验,结果显示,该算法降低了推荐稀疏性,提高了推荐精度。
中图分类号:
| [1]BORCHERS A,HERLOCKER J.Ganging up on informationoverload[J].Computer,1998,31(4):106-108. [2]ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749. [3]YU H,LI J H.A recommendation algorithm to solve the cold start problem of new projects[J].Journal of Software,2015,26(6):1395-1408. [4]PEREIRA A L V,HRUSCHKAE R.Simultaneous co-clustering and learning to address the cold start problem in recommender systems[J].Knowledge- Based Systems,2015,82:11-19. [5]SHAMBOUR Q,LU J.An effective recommender system by unifying user and item trust information for B2B applications[J].Journal of Computer and System Sciences,2015,81(7):1110-1126. [6]LEI M L.Research on shopping behavior based on Alibaba Big Data[J].Internet of Things Technology,2016,6(5):57-60. [7]PAZZANI M J,BILLSUS D.Content-Based RecommendationSystems[C]//Adaptive Web.Springer-Verlag,2007:325-341. [8]BREIMAN L,BREIMAN L,CUTLERR A.Random ForestsMachine Learning[J].Journal of Clinical Microbiology,2001,2:199-228. [9]GOLDBERG D,NICHOLS D A,OKI B M,et al.Using collaborative filtering to weave an information TAPESTRY[J].Communications of the ACM,1992,35(12):61-70. [10]RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C]//Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work.1994:175-186. [11]VERBERT,MANOUSELIS N,OCHOA X,et al.Context-Aware Recommender Systems for Learning:A Survey and Future Challenges[J].IEEE Transactions on Learning Technologies,2012(4):318-335. |
| [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] | 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 |
| [3] | 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 |
| [4] | 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 |
| [5] | 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 |
| [6] | 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 |
| [7] | 孙晓寒, 张莉. 基于评分区域子空间的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace 计算机科学, 2022, 49(7): 50-56. https://doi.org/10.11896/jsjkx.210600062 |
| [8] | 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 |
| [9] | 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 |
| [10] | 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 |
| [11] | 洪志理, 赖俊, 曹雷, 陈希亮, 徐志雄. 基于遗憾探索的竞争网络强化学习智能推荐方法研究 Study on Intelligent Recommendation Method of Dueling Network Reinforcement Learning Based on Regret Exploration 计算机科学, 2022, 49(6): 149-157. https://doi.org/10.11896/jsjkx.210600226 |
| [12] | 郭亮, 杨兴耀, 于炯, 韩晨, 黄仲浩. 基于注意力机制和门控网络相结合的混合推荐系统 Hybrid Recommender System Based on Attention Mechanisms and Gating Network 计算机科学, 2022, 49(6): 158-164. https://doi.org/10.11896/jsjkx.210500013 |
| [13] | 熊中敏, 舒贵文, 郭怀宇. 融合用户偏好的图神经网络推荐模型 Graph Neural Network Recommendation Model Integrating User Preferences 计算机科学, 2022, 49(6): 165-171. https://doi.org/10.11896/jsjkx.210400276 |
| [14] | 余皑欣, 冯秀芳, 孙静宇. 结合物品相似性的社交信任推荐算法 Social Trust Recommendation Algorithm Combining Item Similarity 计算机科学, 2022, 49(5): 144-151. https://doi.org/10.11896/jsjkx.210300217 |
| [15] | 陈壮, 邹海涛, 郑尚, 于化龙, 高尚. 基于用户覆盖及评分差异的多样性推荐算法 Diversity Recommendation Algorithm Based on User Coverage and Rating Differences 计算机科学, 2022, 49(5): 159-164. https://doi.org/10.11896/jsjkx.210300263 |
|
||
首页