计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 168-173.doi: 10.11896/jsjkx.200700101
郝志峰1,2, 廖祥财1, 温雯1, 蔡瑞初1
HAO Zhi-feng1,2, LIAO Xiang-cai1, WEN Wen1, CAI Rui-chu1
摘要: 随着电子商务和互联网的发展,数据信息呈爆炸式增长,协同过滤算法作为一种简单而高效的推荐算法,能在一定程度上有效地解决信息爆炸问题。但是传统协同过滤算法仅通过单一评分来挖掘相似用户,推荐效果并不占优势。为了提高个性化推荐的质量,如何充分利用用户(物品)的文本、图片、标签等上下文信息以使数据价值最大化是当前推荐系统亟待解决的问题。对此,提出了一种融合多种类型上下文信息的协同过滤算法。以用户商品交互信息为二部图,根据不同类型上下文的特点构建不同的相似度网络,设计目标函数在多种上下文信息网络的约束下联合矩阵分解,并学得用户商品的表示学习。在多个数据集上进行了充分实验,结果表明,融合多种类型上下文信息的协同过滤算法不仅能有效提高推荐的准确度,而且能在一定程度上解决数据稀疏性问题。
中图分类号:
[1]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. [2]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37. [3]MNIH A,SALAKHUTDINOV R R.Probabilistic matrix fac-torization[C]//Advances in Neural Information Processing Systems.2008:1257-1264. [4]LUO X,ZHOU M,XIA Y,et al.An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems[J].IEEE Transactions on Industrial Informatics,2014,10(2):1273-1284. [5]SINGH A P ,GORDON G J .Relational Learning via Collective Matrix Factorization[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Las Vegas,Nevada,USA,ACM,2008. [6]BOUCHARD G,YIN D,GUO S.Convex collective matrix factorization[C]//Artificial Intelligence and Statistics.2013:144-152. [7]SINGH A P,GORDON G J.A unified view of matrix factorization models[C]//Joint European Conference on Machine Lear-ning and Knowledge Discovery in Databases.Berlin,Heidelberg:Springer,2008:358-373. [8]LIANG D,ALTOSAAR J,CHARLIN L,et al.Factorizationmeets the item embedding:Regularizing matrix factorization with item co-occurrence[C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:59-66. [9]MIKOLOV T .Distributed Representations of Words and Phrases and their,Compositionality[J].Advances in Neural Information Processing Systems,2013,26:3111-3119. [10]LEVY O ,GOLDBERG Y.Neural word embedding as implicit matrix factorization[J].Advances in Neural Information Processing Systems,2014,3:2177-2185. [11]ZHANG K H,LIANG J Y,ZHAO X W,et al.A collaborative filtering recommendation algorithm based on information of community experts[J].Journal of Computer Research and Development,2018,55(5):968-976. [12]YU Y H,GAO Y,WANG H,et al.Integrating user social status and matrix factorization for item recommendation[J].Journal of Computer Research and Development,2018,55(1):113-124. [13]MOHSEN J,MARTIN E.A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proceedings of the Fourth ACM Conference on Recommender Systems.2010:135-142. [14]LIANG D,ZHAN M,ELLIS D P W.Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks[C]//ISMIR.2015:295-301. [15]AMJAD A,KYLE K,KYUNGHYUN C,et al.Learning distri-buted representations from reviews for collaborative filtering [C]//Proceedings of the 9th ACM Conference on Recommender Systems.2015:147-154. [16]HE R N,MCAULEY J L J.VBPR:visual bayesian personalized ranking from implicit feedback[J].arXiv:1510.01784,2015. [17]KIM D H,PARK C Y,OH J,et al.Convolutional matrix factorization for document context-aware recommendation [C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:233-240. [18]PEROZZI B,AL-RFOU R,SKIENA S.Deepwalk:Online lear-ning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2014:701-710. [19]BARKAN O,KOENIGSTEIN N.Item2vec:neural item embedding for collaborative filtering[C]//2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).IEEE,2016:1-6. [20]SUN X,GUO J,DING X,et al.A general framework for content-enhanced network representation learning[J].arXiv:1610.02906,2016. [21]DONG Y,CHAWLA N V,SWAMI A.Scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017:135-144. [22]SHI C,HU B,ZHAO W X,et al.Heterogeneous information network embedding for recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2018,31(2):357-370. [23]ZHANG F,YUAN N J ,LIAN D ,et al.Collaborative Know-ledge Base Embedding for Recommender Systems[J].KDD,2016:353-362. [24]KOREN Y.Factorization meets the neighborhood:A multiface-ted collaborative filtering model[C]//Proc of the 14th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining (KDD’08).New York:ACM,2008:426-434. [25]MENG X F,DU Z J.Research on the big data fusion:Issues and challenges[J].Journal of Computer Research and Development,2016,53(2):231-246. [26]HU Y,KOREN Y,VOLINSKY C.Collaborative filtering forimplicit feedback datasets[C]//2008 Eighth IEEE International Conference on Data Mining.IEEE,2008:263-272. [27]YANG J H,CHEN C M,WANG C J,et al.HOP-rec:high-order proximity for implicit recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems.2018:140-144. [28]CHEN C M,TSAI M F,LIN Y C,et al.Query-based music recom-mendations via preference embedding[C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:79-82. |
[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 |
|