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Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services 被引量:1

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摘要 The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of interest.Especially,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to users.This is the typical problem of cold start in the field of collaborative filtering recommendation.This problem may makes it difficult for users to locate and acquire the services that they actually want,and the accuracy and novelty of service recommendations are also difficult to satisfy users.To solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper.First,the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation,part-of-speech tagging,and dependency parsing.It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity.Then,a language representation model called Bidirectional Encoder Representation from Transformers(BERT)is used to vectorize the content feature text,and an improved weighted word mover’s distance algorithm based on Term Frequency-Inverse Document Frequency(TFIDF-WMD)is used to calculate the similarity of mobile application services.Finally,the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.The experimental results show that by using the proposed hybrid recommendation method presented in this paper,the cold start problem is alleviated to a certain extent,and the accuracy of the recommendation result has been significantly improved.
出处 《Computers, Materials & Continua》 SCIE EI 2022年第6期6201-6217,共17页 计算机、材料和连续体(英文)
基金 Project supported by the National Natural Science Foundation,China(No.62172123) the Postdoctoral Science Foundation of Heilongjiang Province,China(No.LBH-Z19067) the special projects for the central government to guide the development of local science and technology,China(No.ZY20B11) the Natural Science Foundation of Heilongjiang Province,China(No.QC2018081).
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  • 1PATEREK A.Improving regularized singular value de-composition forcollaborative filtering[C]//Proceedingsof KDD-Cup and Workshop2007.Massachusetts:ACMPress,2007:39-42.
  • 2PAN Rong,ZHOU Yunhong,CAO Bin,et al.One-classcollaborative filtering[C]//Proceedings of ICDM2008.Massachusetts:ACM Press,2008:502-511.
  • 3ADOMAVICIUS G,TUZHILIN A.Toward the next gen-eration of recommender systems:a survey of the state-of-the-art and possible extenstions[J].TKDE,2005,17(6):734-749.
  • 4PAN Rong,MARTIN S.Mind the gaps:weighting theunknow n in large-scale one-class collaborative filtering[C]//Proceedings of KDD2009.Massachusetts:ACMPress,2009:667-675.
  • 5LEE D D,SEUNG H S.Learning the parts of objects bynon-negative matrix factorization[J].Nature,1999,401(675):788-791.
  • 6GUNAWARDANA A,MEEK C.Tied boltzmann ma-chines for cold start recommendations[C]//Proceedingsof RecSys08.Massachusetts:ACM press,2008:19-26.
  • 7GUNAWARDANA A,MEEK C.A unified approach tobuilding hybrid recommender systems[C]//Proceedingsof RecSys09.Massachusetts:ACM Press,2009:117-124.
  • 8PARK S T,CHU W.Pairwise preference regression forcold-start recommendation[C]//Proceedings of Rec-Sys09.Massachusetts:ACM press,2009:21-28.
  • 9CREMONESI P,TURRIN R.Analysis of cold-start rec-ommendations in IPTV systems[C]//Proceedings of Re-cSys09.Massachusetts:ACM Press,2009:1-4.
  • 10PILASZY I,TIKK D.Recommending new movies:e-ven a few ratings are more valuable than metadata[C]//Proceedings of RecSys09.Massachusetts:ACM Press,2009:93-100.

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