计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 70-77.doi: 10.11896/jsjkx.210600011
方义秋1, 张震坤1, 葛君伟2
FANG Yi-qiu1, ZHANG Zhen-kun1, GE Jun-wei2
摘要： 传统的单领域推荐算法受限于用户和项目的稀疏关系,存在用户/项目冷启动的问题,并且,其仅以用户对项目评分进行建模,忽略了评论文本中所蕴含的信息。基于评论文本的跨领域推荐算法在辅助领域提取用户/项目的评论信息来缓解目标领域的数据稀疏问题,以提高推荐的准确率。文中提出了结合自注意力机制和迁移学习的跨领域推荐算法SAMTL(Self-Attention Mechanism and Transfer Learning)。与现有算法不同,SAMTL充分融合了目标领域和辅助领域的知识。首先,引入自注意力机制建模用户的喜好信息;其次,通过交叉映射跨域传输网络实现借助一个领域的信息来提高另一个领域的推荐准确率;最后,在知识融合模块和评分预测模块整合两个域的信息,进行评分预测。在Amazon数据集上的实验表明,与现有的跨领域推荐模型相比,SAMTL的MAE和MSE值更高,在3种不同的跨领域数据集上的MAE值分别提高了8.4%,13.2%和19.4%,MSE值分别提高了6.3%,7.8%和5.6%。通过多项实验验证了自注意力机制和迁移学习的有效性,以及它们在缓解数据稀疏和用户冷启动问题方面的优势。
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| [1]MOONEY R J,ROY L.Content-Based Book RecommendingUsing Learning for Text Categorization[J].arXiv:cs/9902011,1999. [2]BALABANOVIC M,SHOHAM Y.Fab:content-based,collaborative recommendation[J].Communications of the ACM,1997,40(3):66-72. [3]SALAKHUTDINOV R,MNIH A,HINTON G.Restricted Boltzmann machines for collaborative filtering[C]//Proceedings of the 24th International Conference on Machine Learning.2007:791-798. [4]ZHU F,WANG Y,CHEN C,et al.A deep framework for cross-domain and cross-system recommendations[C]//IJCAI International Joint Conference on Artificial Intelligence.2018:3711-3717. [5]KHAN M M,IBRAHIM R,GHANI I.Cross domain recommender systems:a systematic literature review[J].ACM Computing Surveys(CSUR),2017,50(3):1-34. [6]PAN W,XIANG E,LIU N,et al.Transfer learning in collaborative filtering for sparsity reduction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2010:230-235. [7]MAN T,SHEN H,JIN X,et al.Cross-Domain Recommendation:An Embedding and Mapping Approach[C]//IJCAI.2017:2464-2470. [8]WANG X,PENG Z,WANG S,et al.Cross-domain Recommendation for Cold-start Users via Neighborhood based feature mapping[C]//International Conference on Database Systems for Advanced Applications.Cham:Springer,2018:158-165. [9]FU W,PENG Z,WANG S,et al.Deeply Fusing Reviews andContents for Cold Start Users in Cross-Domain Recommendation Systems[J].Proceedings of the AAAI Conference on Artificial Intelligence,2019,33:94-101. [10]LI C,QUAN C,PENG L,et al.A capsule network for recommendation and explaining what you like and dislike[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:275-284. [11]LONI B,SHI Y,LARSON M,et al.Cross-domain collaborative filtering with factorization machines[C]//European Conference on Information Retrieval.Cham:Springer,2014:656-661. [12]LI B,QIANG Y,XUE X.Can movies and books collaborate?cross-domain collaborative filtering for sparsity reduction[C]//Twenty-First International Joint Conference on Artificial Intelligence.2009:2052-2057. [13]LI B,YANG Q,XUE X.Transfer learning for collaborative filtering via a rating-matrix generative model[C]//Proceedings of the 26th Annual International Conference on Machine Learning.2009:617-624. [14]PAN W,XIANG E,YANG Q.Transfer learning in collaborative filtering with uncertain ratings[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2012:662-668. [15]KARATZOGLOU A,AMATRIAIN X,BALTRUNAS L,et al.Multiverse recommendation:N-dimensional tensor factorization for context-aware collaborative filtering[C]//Proceedings of the Fourth ACM Conference on Recommender Systems.2010:79-86. [16]WANG J,LI S J,YANG S,et al.A new transfer learning model for cross-domain recommendation[J].Chinese Journal of Computers,2017,40(33):1-15. [17]SHI Y,LARSON M,HANJALIC A.Tags as bridges between domains:Improving recommendation with tag-induced cross-domain collaborative filtering[C]//International Conference on User Modeling,Adaptation,and Personalization.Berlin:Sprin-ger,2011:305-316. [18]SINGH A P,GORDON G J.Relational learning via collectivematrix factorization[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2008:650-658. [19]ELKAHKY A M,SONG Y,HE X.A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web.2015:278-288. [20]HU L,CAO J,XU G,et al.Personalized recommendation viacross-domain triadic factorization[C]//Proceedings of the 22nd International Conference on World Wide Web.2013:595-606. [21]WU J,XU J,DING T.Fine-grained image classification algo-rithm based on ensemble methods of transfer learning [J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2020,32(3):452-458. [22]PENNINGTON J,SOCHER R,MANNING C D.Glove:Global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing(EMNLP).2014:1532-1543. [23]HU G,ZHANG Y,YANG Q.Conet:Collaborative cross networks for cross-domain recommendation[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.2018:667-676. [24]NAIR V,HINTON G E.Rectified linear units improve restric-ted boltzmann machines[C]//ICML.2010:807-814. [25]SEO S,HUANG J,YANG H,et al.Interpretable convolutional neural networks with dual local and global attention for review rating prediction[C]//Proceedings of the eleventh ACM Confe-rence on Recommender Systems.2017:297-305. [26]CHIN J Y,ZHAN K,JOTY S,et al.ANR:Aspect-based neural recommender[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.2018:147-156. |
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