计算机科学 ›› 2020, Vol. 47 ›› Issue (4): 103-107.doi: 10.11896/jsjkx.190700177
赵楠, 皮文超, 许长桥
ZHAO Nan, PI Wen-chao, XU Chang-qiao
摘要: 近年来,抖音、快手、微视等短视频APP取得了巨大成功,用户拍摄并上传到APP平台上的视频数量暴增。在这种信息过载的环境下,为用户挖掘并推荐其感兴趣的视频成为了视频发布平台面临的难题,因此为这些平台设计高效的视频推荐算法显得尤其重要。文中针对媒体大数据挖掘和推荐领域的数据集稀疏性高和规模巨大的问题,提出一种面向多维特征分析过滤的视频推荐算法。首先,从用户行为和视频标签等多个维度对视频进行特征提取,然后进行相似性分析,加权计算视频相似度,从而获取相似视频候选集,并对相似视频候选集进行过滤,再通过排序选择评分最高的若干个视频推荐给用户。最后,基于MovieLens公开数据集,使用python3语言实现了文中提出的视频推荐算法。在数据集上进行的大量实验表明,相比传统的协同过滤算法,文中提出的面向多维特征分析过滤的视频推荐算法将推荐结果的准确率提升了6%,召回率提升了4%,覆盖率提升了18%。实验数据充分说明,从多个维度考虑视频之间的相似性,并配合大规模矩阵分解技术,在一定程度上缓解了数据集稀疏性高、数据量巨大的难题,从而有效地提高了推荐结果的准确性、召回率和覆盖率。
中图分类号:
[1]ZHENG L S,YANG S Q,HE J,et al.An optimized collaborative filtering recommendation algorithm[C]//2016 2nd International Conference on Cloud Computing and Internet of Things (CCIOT).Dalian,IEEE,2016:89-92. [2]ZHAN M F,LI L,HUANG Q M,et al.Cross-media retrieval with semantics clustering and enhancement[C]//2017 IEEE International Conference on Multimedia and Expo (ICME).Hong Kong,China,IEEE,2017:1398-1403. [3]BRADLEY K,SMYTH B.Improving recommendation diversity [C]// Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science.Irish:AICS,2001:85-94. [4]SU L Y,CHEN X B.Improvement of user-based collaborative filtering algorithms[J].Computer Engineering & Software,2017(4):135-140. [5]HU Y,PENG Q,HU X,et al.Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering[J].IEEE Transactions on Services Computing,2015,8(5):782-794. [6]WANG X M,ZHANG X M,WU Y T,et al.Collaborative filtering recommendation algorithm based on heuristic clustering model and category similarity [J].Journal of Electronic Science,2016,44 (7):1708-1713. [7]KUMAR Y,SHARMA A,KHAUND A,et al.IceBreaker:Solving Cold Start Problem for Video Recommendation Engines[C]//2018 IEEE International Symposium on Multimedia (ISM).IEEE,2018:217-222. [8]PATRA B K,LAUNONEN R,OLLIKAINEN V,et al.Exploiting bhattacharyya similarity measure to diminish user cold-start problem in sparse data[M]//Discovery Science.Cham:Springer,2014:252-263. [9]HUANG C G,YIN J,WANG J,et al.Uncertain neighbors collaborative filtering recommendation algorithm[J].Chinese Journal of Computers,2010,33(8):1369-1377. [10]HE Y,YANG S,JIAO C,et al.A Hybrid Collaborative Filtering Recommendation Algorithm for Solving the Data Sparsity[C]// 2011 International Symposium on Computer Science and Society.2011:118-121. [11]WANG P,YE H W.A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering [C]// 2009 International Conference on Industrial and Information Systems.2009:152-154. [12]CACHEDA F,FORMOSO V.Comparison of collaborative filtering algorithms:Limitations of current techniques and proposals for scalable,high -performance recommender systems [J].ACM Transactions on the Web,2011,5(1):1-33. [13]DESHPANDE M,KARYPIS G.Item-based top-N recommendation algorithms[J].ACM Transactionon Information Systems,2004,22(1):143-177. [14]WANG P,QIAN Q,SHANG Z,et al.An recommendation algorithm based on weighted Slope one algorithm and user-based collaborative filtering [C]// 2016 Chinese Control and Decision Conference (CCDC).2016:2431-2434. [15]ZARZOUR H,AL-SHARIF Z,AL-AYYOUB M,et al.A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques [C]// 2018 9th International Conference on Information and Communication Systems (ICICS).2018:102-106. [16]ARORA S,GOEL S.Improving the Accuracy of Recommender Systems Through Annealing[J].Lecture Notes in Networks and Systems,2017,1(1):295-304. [17]KOREN Y,BELL R,VOLINSKY C.Matrix Factorization Techniques for Recommender Systems[J].Computer,2009,42(8):30-37. [18]SHEUGH L,ALIZADEH S H.A note on pearson correlation coefficient as a metric of similarity in recommender system [C]// 2015 AI & Robotics (IRANOPEN).2015:1-6. |
[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] | 李霞, 马茜, 白梅, 王习特, 李冠宇, 宁博. RIIM:基于独立模型的在线缺失值填补 RIIM:Real-Time Imputation Based on Individual Models 计算机科学, 2022, 49(8): 56-63. https://doi.org/10.11896/jsjkx.210600180 |
[4] | 张源, 康乐, 宫朝辉, 张志鸿. 基于Bi-LSTM的期货市场关联交易行为检测方法 Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM 计算机科学, 2022, 49(7): 31-39. https://doi.org/10.11896/jsjkx.210400304 |
[5] | 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 |
[6] | 曾志贤, 曹建军, 翁年凤, 蒋国权, 徐滨. 基于注意力机制的细粒度语义关联视频-文本跨模态实体分辨 Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism 计算机科学, 2022, 49(7): 106-112. https://doi.org/10.11896/jsjkx.210500224 |
[7] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
[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] | 刘伟业, 鲁慧民, 李玉鹏, 马宁. 指静脉识别技术研究综述 Survey on Finger Vein Recognition Research 计算机科学, 2022, 49(6A): 1-11. https://doi.org/10.11896/jsjkx.210400056 |
[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] | 高元浩, 罗晓清, 张战成. 基于特征分离的红外与可见光图像融合算法 Infrared and Visible Image Fusion Based on Feature Separation 计算机科学, 2022, 49(5): 58-63. https://doi.org/10.11896/jsjkx.210200148 |
[13] | 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 |
[14] | 左杰格, 柳晓鸣, 蔡兵. 基于图像分块与特征融合的户外图像天气识别 Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion 计算机科学, 2022, 49(3): 197-203. https://doi.org/10.11896/jsjkx.201200263 |
[15] | 任首朋, 李劲, 王静茹, 岳昆. 基于集成回归决策树的lncRNA-疾病关联预测方法 Ensemble Regression Decision Trees-based lncRNA-disease Association Prediction 计算机科学, 2022, 49(2): 265-271. https://doi.org/10.11896/jsjkx.201100132 |
|