计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 113-118.doi: 10.11896/jsjkx.200900067
所属专题： 大数据&数据科学 虚拟专题
肖诗涛1, 邵蓥侠1, 宋卫平2, 崔斌2
XIAO Shi-tao1, SHAO Ying-xia1, SONG Wei-ping2, CUI Bin2
摘要： 协同过滤技术在现代推荐系统中得到了广泛的应用,其基本思想是相似的用户会喜欢相似的物品。评分函数(Score Function,SF)是协同过滤推荐模型的一个关键技术,用于评估用户对物品的喜好程度。然而,目前常用的评分函数存在如下缺陷,即内积评分函数难以有效捕捉用户与用户以及物品与物品的相似度,而欧几里德距离度量函数由于几何空间限制降低了模型的表达能力。文中提出了一种融合内积相似度和欧几里德距离度量的新颖的混合评分函数,并从理论上分析了此混合评分函数的性质,证明它能有效弥补现有评分函数的不足。此外,新的混合评分函数是一项通用技术,适用于诸多现有的推荐模型(如SVD++,MF,NGCF,CML等),能够提高模型的推荐质量。最后,在6个公开数据集上进行了大量实验,验证了新混合评分函数的优越性能。
中图分类号:
| [1]YI X,YANG J,HONG L,et al.Sam-pling-bias-corrected neural modeling for large corpus item recommendations[C]//Procee-dings of ACM Conference on Recommender Systems.Copenha-gen:Association for Computing Machinery,2019:269-277. [2]EBESU T,SHEN B,FANG Y.Collaborative Memory Network for Recommendation Systems[C]//Proceedings of the International ACM SIGIR Conference.Ann Arbor:Association for Computing Machinery,2018:515-524. [3]KOREN Y,BELL R,VOLINSKY C.Matrix Factorization Tech-niques for Recommender Systems[J].Computer,2009,42(8):30-37. [4]CHEN X,XU H,ZHANG Y,et al.Sequential Recommendation with User Memory Net-works[C]//Proceedings of the ACM International Conference on Web Search and Data Mining.Marina Del Rey:Association for Computing Machinery,2018:108-116. [5]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]//Proceedings of the Conference on Uncertainty in Artificial Intelligence.Montreal:AUAI Press,2009:452-461. [6]ZHENG L,LU C,JIANG F,et al.Spectral col-laborative filtering[C]//Proceedings of ACM Conference on Recommender Systems.Vancouver:Association for Computing Machinery,2018:311-319. [7]HE X,LIAO L,ZHANG H,et al.Neural Collabora-tive Filtering[C]//Proceedings of the International Conference on World Wide Web.Perth:International World Wide Web Conferences Steering Committee,2017:173-182. [8]HSIEH C,YANG L,CUI Y,et al.Collaborative Metric Lear-ning[C]//Proceedings of the International Conference on World Wide Web.Perth:International World Wide Web Conferences Steering Committee,2017:193-201. [9]XIAO H,HUANG M,ZHU X.From one point to a manifold:knowledge graph embedding for precise link prediction[C]//Proceedings of the Conference on Uncertainty in Artificial Intelligence.New York:AUAI Press,2016:1315-1321. [10]WANG X,HE X,WANG M,et al.Neural Graph Collaborative Filtering[C]//Proceedings of the International ACM SIGIR Conference.Paris:Association for Computing Machinery,2019:165-174. [11]WANG H,WANG N,YEUNG D.Collaborative Deep Learning for Recommender Systems [C]//Proceedings of the ACM SIGKDD International Conference.Sydney:Association for Computing Machinery,2015:1235-1244. [12]KHOSHNESHIN M,STREET W.Collaborative filtering viaeuclidean embedding[C]//Proceedings of ACM Conference on Recommender Systems.Barcelona:Association for Computing Machinery,2010:87-94. [13]CEN Y,ZOU X,ZHANG J,et al.Representation Learning for Attributed Multiplex Heterogeneous Net-work[C]//Procee-dings of the ACM SIGKDD International Conference.Ancho-rage:Association for Computing Machinery,2019:1358-1368. [14]MASSA P,AVESANI P.Trust-aware recom-mender systems[C]//Proceedings of ACM Conference on Recommender Systems.Minneapolis:Association for Computing Machinery,2007:17-24. [15]ZIEGLER C,MCNEE S,KONSTAN J,et al.Improving recommendation lists through topic diversi-fication[C]//Proceedings of the International Conference on World Wide Web.Chiba:International World Wide Web Conferences Steering Committee,2005:22-32. [16]MCAULEY J,TARGETT C,SHI Q,et al.Image-Based Recommendations on Styles and Substitutes[C]//Proceedings of the International ACM SIGIR Conference.Santiago:Association for Computing Machinery,2015:43-52. [17]HE X,CHEN T,KAN M,et al.TriRank:Review-aware Ex-plainable Recommendation by Model-ing Aspects[C]//Procee-dings of the ACM International on Conference on Information and Knowledge Management.Melbourne:Association for Computing Machinery,2015:1661-1670. [18]HERLOCKER J,KONSTAN J,BORCHERS A,et al.An algorithmic framework for performing collaborative filtering[C]//Proceedings of the International ACM SIGIR Conference.Berkeley:Association for Computing Machinery,1999:230-237. [19]SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]//Procee-dings of the International Conference on World Wide Web.Hong Kong:International World Wide Web Conferences Steering Committee,2001:285-295. [20]KOREN Y.Factorization meets the neighborhood:a multiface-ted collaborative filtering model[C]//Proceedings of the ACM SIGKDD International Conference.Las Vegas:Association for Computing Machinery,2008:426-434. |
| [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] | 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 |
| [11] | 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 |
| [12] | 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 |
| [13] | 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 |
| [14] | 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 |
| [15] | 陈晋鹏, 胡哈蕾, 张帆, 曹源, 孙鹏飞. 融合时间特性和用户偏好的卷积序列化推荐 Convolutional Sequential Recommendation with Temporal Feature and User Preference 计算机科学, 2022, 49(1): 115-120. https://doi.org/10.11896/jsjkx.201200192 |
|
||
首页