计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 102-108.doi: 10.11896/jsjkx.230600078
王子泓1, 邵蓥侠1, 何吉元2, 刘金宝2
WANG Zihong1, SHAO Yingxia1, HE Jiyuan2, LIU Jinbao2
摘要: 序列推荐旨在从用户的历史行为中建模用户不断变化的兴趣,从而做出与用户兴趣相关的推荐。近年来,物品属性信息被证明可以提升序列推荐的性能,很多工作基于属性信息融合去提升序列推荐的性能,都取得了成效但仍存在一定的不足。首先,它们没有显式地建模出用户对物品属性的偏好或者只建模了一个属性偏好向量,无法充分表达用户的偏好。其次,它们的物品属性信息融合过程未考虑用户个性化信息的影响。因此,针对上述不足,提出了基于多空间属性信息融合的序列推荐(MAIF-SR)。文中提出了多空间属性信息融合框架,在不同的属性空间下融合属性序列并建模出用户对不同属性的偏好,用多维兴趣充分表达用户的偏好;设计了个性化属性注意力机制,在融合信息的过程中引入用户个性化信息,增强融合信息的个性化效果。在两个公开数据集以及一个工业私有数据集上进行实验,结果表明,MAIF-SR优于用于对比的基于属性信息融合的序列推荐。
中图分类号:
[1]YU F,LIU Q,WU S,et al.A dynamic recurrent model for next basket recommendation[C]//Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval.2016:729-732. [2]LIU Q,WU S,WANG D,et al.Context-aware sequential recommendation[C]//2016 IEEE 16th International Conference on Data Mining(ICDM).IEEE,2016:1053-1058. [3]WU Q,GAO Y,GAO X,et al.Dual sequential prediction models linking sequential recommendation and information dissemination[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:447-457. [4]KANG W C,MCAULEY J.Self-attentive sequential recommendation[C]//2018 IEEE International Conference on Data Mi-ning(ICDM).IEEE,2018:197-206. [5]XIN X,HE X,ZHANG Y,et al.Relational collaborative filtering:Modeling multiple item relations for recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:125-134. [6]ZHANG T,ZHAO P,LIU Y,et al.Feature-level Deeper Self-Attention Network for Sequential Recommendation[C]//IJCAI.2019:4320-4326. [7]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[J].arXiv:1706.03762,2017. [8]ZHOU K,WANG H,ZHAO W X,et al.S3-rec:Self-supervised learning for sequential recommendation with mutual information maximization[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management.2020:1893-1902. [9]LIU C,LI X,CAI G,et al.Noninvasive self-attention for side information fusion in sequential recommendation[C]//Procee-dings of the AAAI Conference on Artificial Intelligence.2021:4249-4256. [10]XIE Y,ZHOU P,KIM S.Decoupled side information fusion for sequential recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.2022:1611-1621. [11]HE R N,MCAULEY J.Fusing similarity models with markov chains for sparse sequential recommendation[C]//2016 IEEE 16th International Conference on Data Mining(ICDM).IEEE,2016:191-200. [12]KABBUR S,NING X,KARYPIS G.Fism:factored item simila-rity models for top-n recommender systems[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2013:659-667. [13]TANG J,WANG K.Personalized top-n sequential recommendation via convolutional sequence embedding[C]//Proceedings of the eleventh ACM International Conference on Web Search and Data Mining.2018:565-573. [14]YUAN F,KARATZOGLOU A,ARAPAKIS I,et al.A simple convolutional generative network for next item recommendation[C]//Proceedings of the Twelfth ACM International Confe-rence on Web Search and Data Mining.2019:582-590. [15]QUADRANA M,KARATZOGLOU A,HIDASI B,et al.Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the Eleventh ACM Conference on Recommender Systems.2017:130-137. [16]YAN A,CHENG S,KANG W C,et al.CosRec:2D convolu-tional neural networks for sequential recommendation[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.2019:2173-2176. [17]ZHENG L,FAN Z,LU C T,et al.Gated spectral units:Mode-ling co-evolving patterns for sequential recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:1077-1080. [18]CHANG J,GAO C,ZHENG Y,et al.Sequential recommendation with graph neural networks[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:378-387. [19]SUN F,LIU J,WU J,et al.BERT4Rec:Sequential recommendation with bidirectional encoder representations from transformer[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.2019:1441-1450. [20]WU L,LI S,HSIEH C J,et al.SSE-PT:Sequential recommendation via personalized transformer[C]//Proceedings of the 14th ACM Conference on Recommender Systems.2020:328-337. [21]LIN J,PAN W,MING Z.FISSA:fusing item similarity models with self-attention networks for sequential recommendation[C]//Proceedings of the 14th ACM Conference on Recommender Systems.2020:130-139. [22]FAN X,LIU Z,LIAN J,et al.Lighter and better:low-rank decomposed self-attention networks for next-item recommendation[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:1733-1737. [23]ZHOU G,ZHU X,SONG C,et al.Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2018:1059-1068. [24]ZHOU C,BAI J,SONG J,et al.Atrank:An attention-based user behavior modeling framework for recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2018,32(1). [25]COVINGTON P,ADAMS J,SARGIN E.Deep neural networks for youtube recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:191-198. [26]ZHOU G,BIAN W,WU K,et al.Can:Revisiting feature co-action for click-through rate prediction[J].arXiv:2011.05625,2020. |
|