计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 178-184.doi: 10.11896/jsjkx.220600024
董家玮, 孙福振, 吴相帅, 吴田慧, 王绍卿
DONG Jia-wei, SUN Fu-zhen, WU Xiang-shuai, WU Tian-hui, WANG Shao-qing
摘要： 目前基于哈希技术的推荐算法常用汉明距离表示用户和项目哈希码的相似性,但忽略了哈希码中每位的潜在区别信息,为此提出了一个差异性汉明距离,通过考虑哈希码之间的差异性为哈希码赋予位权重;为差异性汉明距离设计了一个变分推荐模型,该模型分为用户哈希组件和项目哈希组件两部分,以变分自编码器结构连接。首先,模型利用编码器为用户和项目生成哈希码,为提高哈希码的鲁棒性,在哈希码中加入高斯噪声。其次,通过差异性汉明距离优化用户和项目哈希码,以最大限度地提高模型重构用户-项目评分的能力。在两个公开的数据集上的实验结果表明,在计算开销不变的前提下与最先进的哈希推荐算法相比,所提模型在NDCG上提高了3.9%,在MRR上提高了4.7%。
中图分类号:
| [1]WANG Z S,LI Q,WANG J,et al.Real-Time Personalized Re-commendation Based on Implicit User Feedback Data Stream[J].Chinese Journal of Computers,2016,39(1):53-64. [2]HUANG C L,LU Y X.Research on Hybrid Music Recommendation Algorithm based on Collaborative Filtering and Tags[J].Software Engineering,2021,24(4):10-14. [3]YEHUDA K,ROBERT B,CHRIS V.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37. [4]LI H Q,WANG Y X,CHEN Z D,et al.Ranking-Based Supervised Discrete Cross-Modal Hashing[J].Chinese Journal of Computers,2021,44(8):1620-1635. [5]WU Z B,YU J Q,HE Y F,et al.Multi-level Semantic Binary Descriptor for Image Retrieval[J].Chinese Journal of Compu-ters,2020,43(9):1641-1655. [6]ZHANG Z W,WANG Q F,RUAN L Y,et al.Preference preserving hashing for efficient recommendation[C]//Proceedings of the 37th International ACM SIGIR Conference on Research &Development in Information Retrieval.2014:183-192. [7]ZHOU K,ZHA H Y.Learning binary codes for collaborative filtering[C]//Proceedings of the 18th ACM SIGKDD Interna-tional Conference on Knowledge Discovery and Data Mining.2012:498-506. [8]SALAKHUTDINOV R,HINTON G.Semantic hashing [J].International Journal of Approximate Reasoning,2009,50(7):969-978. [9]ZOU A,HAO W N,JIN D W,et al.Study on Text RetrievalBased on Pre-training and Deep Hash[J].Computer Science,2021,48(11):300-306. [10]CHEN Q,DAI Y W,LIU G J.Research on KPI anomaly detection model for intelligent operation and maintenance[J].Journal of Chongqing University of Technology(Natural Science),2022,36(6):181-188. [11]ZHANG H W,SHEN F M,LIU W,et al.Discrete Collaborative Filtering[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA,2016:325-334. [12]HUANG X L,CHEN C M,LIU G H.A hybrid second-order total variational noise reduction method for radiation-resistant images[J].Journal of Chongqing University of Technology(Natural Science),2022,34(4):585-594. [13]CHAIDAROON S,FANG Y.Variational deep semantic hashing for text documents[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval.2017:75-84. [14]ZHANG L,ZHANG Y D,TANG J H,et al.Binary code ranking with weighted Hamming distance[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2013:1586-1593. [15]LIU C H,LU T,WANG X,et al.Compositional Coding for Collaborative Filtering[C]//International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:145-154. [16]CHEN W J.An Adaptive Approach for Knowledge Representation Fused with Topic Feature[J].Computer Engineering,2021,47(1):87-93,100. [17]OOSTERHUIS H,RIJKE M.Unifying Online and Counterfactual Learning to Rank:A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions[C]//Proceedings of the 14th ACM International Conference on Web Search and Data Mining(WSDM ’21).Association for Computing Machinery,New York,USA,2021:463-471. [18]CHAIDAROON S,EBESU T,FANG Y.Deep Semantic Text Hashing with Weak Supervision[C]//International ACM SIGIR Conference on Research and Development in Information Retrieval.2018:1109-1112. [19]ZHAO B Y,WANG L S,ZHANG M l,et al.Random PlaintextCollision Attack Against AES Algorithm with Reused Masks[J].Computer Engineering,2022,48(6):139-145,153. [20]HANSEN C,HANSEN C,SIMONSEN J G,et al.Content-aware Neural Hashing for Cold-start Recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.Association for Computing Machinery,New York,USA,2020:971-980. [21]LIAN D F,LIU R,GE Y,et al.Discrete Content-aware Matrix Factorization[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017:325-334. [22]ZHANG Y,YIN H Z,HUANG Z,etal.Discrete Deep Learning for Fast Content-Aware Recommendation[C]//ACM International Conference on Web Search and Data Mining.2018:717-726. [23]SUN Y,PAN J W,ZHANG A,et al.FM2:Field-matrixed Factorization Machines for Recommender Systems[C]//Procee-dings of the Web Conference 2021(WWW ’21).Association for Computing Machinery,New York,USA,2021:2828-2837. [24]GUO F Q,MENG F R,WANG Z X.Rumor Stance Classification Algorithm Based on Variational Auto-Encoder[J].Compu-ter Engineering,2022,48(2):99-105. |
| [1] | 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 |
| [2] | 窦家维. 保护隐私的汉明距离与编辑距离计算及应用 Privacy-preserving Hamming and Edit Distance Computation and Applications 计算机科学, 2022, 49(9): 355-360. https://doi.org/10.11896/jsjkx.220100241 |
| [3] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
| [4] | 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 |
| [5] | 王美玲, 刘晓楠, 尹美娟, 乔猛, 荆丽娜. 基于评论和物品描述的深度学习推荐算法 Deep Learning Recommendation Algorithm Based on Reviews and Item Descriptions 计算机科学, 2022, 49(3): 99-104. https://doi.org/10.11896/jsjkx.210200170 |
| [6] | 唐雨潇, 王斌君. 基于深度生成模型的人脸编辑研究进展 Research Progress of Face Editing Based on Deep Generative Model 计算机科学, 2022, 49(2): 51-61. https://doi.org/10.11896/jsjkx.210400108 |
| [7] | 伍美霖, 黄佳进, 秦进. 用于协同过滤的序列解耦变分自编码器 Disentangled Sequential Variational Autoencoder for Collaborative Filtering 计算机科学, 2022, 49(12): 163-169. https://doi.org/10.11896/jsjkx.211200080 |
| [8] | 董云薪, 林耿, 张清伟, 陈颖婷. 基于Apriori算法填充数据及改进相似度的推荐算法 Recommendation Algorithm Based on Apriori Algorithm and Improved Similarity 计算机科学, 2022, 49(11A): 211000005-5. https://doi.org/10.11896/jsjkx.211000005 |
| [9] | 富坤, 郭云朋, 禚佳明, 李佳宁, 刘琪. 语义增强的完全不平衡标签网络表示学习算法 Semantic Information Enhanced Network Embedding with Completely Imbalanced Labels 计算机科学, 2022, 49(11): 109-116. https://doi.org/10.11896/jsjkx.210900101 |
| [10] | 董晓梅, 王蕊, 邹欣开. 面向推荐应用的差分隐私方案综述 Survey on Privacy Protection Solutions for Recommended Applications 计算机科学, 2021, 48(9): 21-35. https://doi.org/10.11896/jsjkx.201100083 |
| [11] | 赵金龙, 赵中英. 基于异质信息网络表示学习与注意力神经网络的推荐算法 Recommendation Algorithm Based on Heterogeneous Information Network Embedding and Attention Neural Network 计算机科学, 2021, 48(8): 72-79. https://doi.org/10.11896/jsjkx.200800226 |
| [12] | 张仁杰, 陈伟, 杭梦鑫, 吴礼发. 基于变分自编码器的不平衡样本异常流量检测 Detection of Abnormal Flow of Imbalanced Samples Based on Variational Autoencoder 计算机科学, 2021, 48(7): 62-69. https://doi.org/10.11896/jsjkx.200600022 |
| [13] | 熊旭东, 杜圣东, 夏琬钧, 李天瑞. 基于二分图卷积表示的推荐算法 Recommendation Algorithm Based on Bipartite Graph Convolution Representation 计算机科学, 2021, 48(4): 78-84. https://doi.org/10.11896/jsjkx.200400023 |
| [14] | 富坤, 赵晓梦, 付紫桐, 高金辉, 马浩然. 基于不完全信息的深度网络表示学习方法 Deep Network Representation Learning Method on Incomplete Information Networks 计算机科学, 2021, 48(12): 212-218. https://doi.org/10.11896/jsjkx.201000015 |
| [15] | 陈源毅, 冯文龙, 黄梦醒, 冯思玲. 基于知识图谱的行为路径协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph 计算机科学, 2021, 48(11): 176-183. https://doi.org/10.11896/jsjkx.201000004 |
|
||
首页