基于改进内容过滤算法的高校图书馆文献资源个性化推荐研究
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摘要
[目的 /意义]基于内容的过滤推荐中,针对向量空间模型表示文本时容易造成维度灾难的问题,提出利用余弦值r与匹配度值Sim相结合的方法对原有模型进行改进。[方法 /过程]由文献资源和用户兴趣分别筛选出权重较大特征词的词向量,进而由公式计算余弦值r,结合对应的特征词权重进一步计算出匹配度值Sim,将其作为向目标用户推荐文献的依据,并利用河北工业大学图书馆的相关数据对改进模型、向量空间模型及LDA主题模型进行实验,最后利用查准率、召回率、F1值及运行时间等评价指标对3种模型的实验结果进行分析。[结果 /结论]实验结果表明所提出的改进模型相比较于实验中的向量空间模型与LDA主题模型具有更高的应用价值与运行效率。
[Purpose/significance]In content-based filtering recommendation,the problem of dimensionality disaster is easily caused when the vector space model (VSM) is used to represent text. This paper proposes a method that combines the cosine value r and the matching value Sim to improve the original model. [Method/process]based on literature resources and user interests the word vectors of feature words with large weight were selected,and then the cosine value r is calculated by the formula,and the matching value Sim is further calculated based on the corresponding feature words weights as the basis for recommending literature to the target user. And it uses the data from the Hebei University of Technology Library to conduct experiments on the improved model,vector space model and LDA topic model,and finally uses the evaluation index of precision rate,recall rate,F1 and running time to analysis the experimental results of the three models. [Result/conclusion] The experimental results show that the improved model presented in this paper has higher application value and operation efficiency compared with the vector space model and LDA topic model.
[Purpose/significance]In content-based filtering recommendation,the problem of dimensionality disaster is easily caused when the vector space model (VSM) is used to represent text. This paper proposes a method that combines the cosine value r and the matching value Sim to improve the original model. [Method/process]based on literature resources and user interests the word vectors of feature words with large weight were selected,and then the cosine value r is calculated by the formula,and the matching value Sim is further calculated based on the corresponding feature words weights as the basis for recommending literature to the target user. And it uses the data from the Hebei University of Technology Library to conduct experiments on the improved model,vector space model and LDA topic model,and finally uses the evaluation index of precision rate,recall rate,F1 and running time to analysis the experimental results of the three models. [Result/conclusion] The experimental results show that the improved model presented in this paper has higher application value and operation efficiency compared with the vector space model and LDA topic model.
引文
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[2]刘旭晖.融合主题多样性与影响力的科技文献推荐算法研究[J].情报理论与实践,2017,40(12):134-138.
[3]刘伟,刘柏嵩,王洋洋.海量学术资源个性化推荐综述[J].计算机工程与应用,2018,54(3):30-39.
[4]王超,吕俊生.国内外学术信息推荐方法研究进展[J].情报杂志,2013,32(9):142-147.
[5]阮光册,夏磊.推荐系统的发展与公共图书馆个性化信息服务探讨[J].图书馆,2016(2):94-99.
[6]BEEL J,GIPP B,LANGER S,et al.Research-paper recommender systems:a literature survey[J].International journal on digital libraries,2015,17(4):1-34.
[7]PHILIP S,MUSA E P.A paper recommender system based on the past ratings of a user[J].International journal of advanced computer technology(IJACT),2014,3(6):41-46.
[8]黄震华,张佳雯,张波,等.语义推荐算法研究综述[J].电子学报,2016,44(9):2262-2275.
[9]徐勇,司凤山,吴延辉,等.基于概念泛化的科技文献推荐算法[J].图书情报工作,2012,56(21):101-108.
[10]吕学强,王腾,李雪伟,等.基于内容和兴趣漂移模型的电影推荐算法研究[J].计算机应用研究,2018,35(3):717-720,802.
[11]安悦,李兵,杨瑞泰,等.基于内容的热门微话题个性化推荐研究[J].情报杂志,2014,33(2):155-160.
[12]丁德红,方逵,王娟,等.基于内容过滤推荐的农业信息推荐模型研究[J].湖南农业大学学报(自然科学版),2013,39(6):683-687,562.
[13]雷凯,刘树波,李丹,等.实时路况制约下基于内容的兴趣点推荐[J].计算机工程,2017,43(10):147-152.
[14]LIU L,LECUE F,MEHANDJIEV N.Semantic content-based recommendation of software services using context[J].ACM transactions on the web(TWEB),2013,7(3):1-20.
[15]DELDJOO Y,ELAHI M,CREMONESI P,et al.Content-based video recommendation system based on stylistic visual features[J].Journal on data semantics,2016,5(2):99-113.
[16]LEE S,CHOI K,SUH Y.A personalized trustworthy seller recommendation in an open market[J].Expert systems with applications,2013,40(4):1352-1357.
[17]LIU N H.Comparison of content-based music recommendation using different distance estimation methods[J].Applied intelligence,2013,38(2):160-174.
[18]施聪莺,徐朝军,杨晓江.TFIDF算法研究综述[J].计算机应用,2009,29(s1):167-170.
[19]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].Computer science,2013:ar Xiv:1301.3781.
[20]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed representations of words and phrases and their compositionality[C]//Advances in neural information processing systems.Nevada:Curran Associates Inc.2013:3111-3119.
[21]LAI S,LIU K,HE S,et al.How to generate a good word embedding[J].IEEE intelligent systems,2016,31(6):5-14.
[22]王飞,谭新.一种基于Word2Vec的训练效果优化策略研究[J].计算机应用与软件,2018,35(1):97-102,174.